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The fitness value of an individual (genotype or a chromosome) is a positive number that is a measure of its goodness. When the chromosome represents a solution to the combinatorial optimization problem, the fitness value indicates the cost of the solution. In the case of a minimization problem, solutions with lower cost correspond to individuals that are more fit. GAs operate on a population (or set) of individuals (or solutions) encoded as strings. These strings represent points in the search space. In each iteration, referred to as a generation, a new set of strings that represent solutions (called offspring) is created by crossing some of the strings of the current generation [10]. Occasionally, new characteristics are injected to add diversity. GAs combine information exchange along with survival of the fittest among individuals to conduct the search. When employing GAs to solve a combinatorial optimization problem one has to find an efficient representation of the solution in the form of a chromosome. Associated with each chromosome is its fitness value. If we simulate the process of natural reproduction, combined with the biological principle of survival of the fittest, then, as each generation progresses, better and better individuals (solutions) with higher fitness values are expected to be produced. Because GAs work on a population of solutions, an initial population constructor is required to generate a certain predefined number of solutions. The quality of the final solution produced by a genetic algorithm depends on the size of the population and how the initial population is constructed. The initial population generally comprises random solutions. The population of chromosomes evolves from generation to the next through the use of two types of genetic operators: (1) unary operators such as mutation and inversion, which alter the genetic structure of a single chromosome, and (2) higher order operator, referred to as crossover, which consists of obtaining new individual by combining genetic material from two selected parent chromosomes. The resulting individuals produced when genetic operators are applied on the parents are termed as offspring. Then the new population is selected out of the individuals of the current population and its offspring. The choice of parents for crossover from the set of individuals that comprise the population is probabilistic. In keeping with the ideas of natural selection, we assume that stronger individuals, i.e., those with higher fitness values, are more likely to mate than the weaker ones. One way to simulate this is to select parents with a probability that is directly proportional to their fitness values. Larger the fitness, the greater is chance of an individual being selected as one of the parents for crossover [10]. Several crossover operators have been proposed in the literature. Depending on the combinatorial optimization problem being solved some are more effective than others. One popular crossover that will also help illustrate the concept is the simple crossover. It performs the “cut-catenate” operation. It consists of choosing a random cut point and dividing each of the two chromosomes into two parts. The offspring is then generated by catenating the segment of one parent to the left of the cut point with the segment of the second parent to the right of the cut point. Mutation (µ) produces incremental random changes in the offspring by randomly changing allele values of some genes. In case of binary chromosomes it corresponds to changing single bit positions. It is not applied to all members of the population, but is applied probabilistically only to some. Mutation has the effect of perturbing a certain chromosome in order to introduce new characteristics not present in any element of the parent population. For example, in case of binary chromosomes, toggling some selected bits produces the desired effect. Inversion is the third operator of GA and like mutation it also operates on a single chromosome. Its basic function is to laterally invert the order of alleles between two randomly chosen points on a chromosome. A generation is an iteration of GA where individuals in the current population are selected for crossover and offspring are created. Due to the addition of offspring, the size of population increases. In order to keep the number of members in a population fixed, a constant number of individuals are selected from this set that consists of both the individuals of the initial population, and the generated offspring. If M is the size of the initial population and N 0 is the number of offspring created in each generation, then, before the beginning of next generation, M new parents from M + N 0 individuals are selected. A greedy © 2002 by CRC Press LLC
FIGURE 39.76 Structure of a simple genetic algorithm. selection mechanism is to choose the best M individuals from the total of M + N 0. The complete pseudo code of a simple GA is given in Fig. 39.76. Tabu Search The previous subsection discussed simulated annealing, which was inspired by the cooling of metals, and genetic algorithms, which imitate the biological phenomena of evolutionary reproduction. In this section we present a more recent optimization method called tabu search (TS), which is based on selected concepts of artificial intelligence (AI). Tabu search was introduced by Fred Glover [11–14] as a general iterative heuristic for solving combinatorial optimization problems. Initial ideas of the technique were also proposed by Hansen [15] in his steepest ascent mildest descent heuristic. TS is conceptually simple and elegant. It is a form of local neighborhood search. Each solution S ∈ Ω has an associated set of neighbors N(S) ⊆ Ω. A solution S′ ∈ N(S) can be reached from S by an operation called a move to S′. Normally, the neighborhood relation assumed symmetric. That is, if S′ is a neighbor of S then S is a neighbor of S′. At each step, the local neighborhood of the current solution is explored and the best solution in that neighborhood is selected as the new current solution. Unlike local search that stops when no improved new solution is found in the current neighborhood, tabu search continues the search from the best solution in the neighborhood even if it is worse than the current solution. To prevent cycling, information pertaining to the most recently visited solutions are inserted in a list called tabu list. Moves to tabu solutions are not allowed. The tabu status of a solution is overridden when certain criteria (aspiration criteria) are satisfied. One example of an aspiration criterion is when the cost of the selected solution is better than the best seen so far, which is an indication that the search is actually not cycling back, but rather moving to a new solution not encountered before. Tabu search is a metaheuristic, which can be used not only to guide the search in complex solution spaces, but also to direct the operations of other heuristic procedures. It can be superimposed on any heuristic whose operations are characterized as performing a sequence of moves that lead the procedure from one trial solution to another. In addition to several other characteristics, the attractiveness of tabu search comes from its ability to escape local optima. Tabu search differs from SA or GA, which are memoryless, and also from branch-and-bound, A ∗ search, etc., which are rigid memory approaches. One of its features is its systematic use of adaptive © 2002 by CRC Press LLC
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© 2002 by CRC Press LLC
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© 2002 by CRC Press LLC Fernanda p
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Locating Your Topic Several avenues
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Krste Asanovic Massachusetts Instit
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7 8 9 Architectures for Low Power P
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SECTION VII Communications and Netw
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45 Testing of Synchronous Sequentia
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Hiroshi Iwai Tokyo Institute of Tec
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FIGURE 1.2 FIGURE 1.3 © 2002 by CR
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1.2 Downsizing below 0.1 © 2002 by
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FIGURE 1.9 Subthreshold leakage cur
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FIGURE 1.13 Epitaxial channel [9].
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gate length reduction was accelerat
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of the prediction. Until only sever
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FIGURE 1.22 Clarke number of elemen
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1.5 Source and Drain Figure 1.25 sh
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FIGURE 1.27 Retrograde profile. FIG
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TABLE 1.6 Trend of Interconnect by
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TABLE 1.9a Trend of DRAM Cell: Stac
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FIGURE 1.36 Trend of gate length. F
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References 1. D. Kahng and M. M. At
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40. B. H. Lee, R. Choi, L. Kang, S.
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outputs of the gate regardless of t
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FIGURE 2.4 circuit. This is the bas
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FIGURE 2.8 Two different realizatio
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FIGURE 2.12 Multifunction circuitry
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Dynamic Logic Circuits The basic id
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transistors in the pull-down networ
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In Fig. 2.21(d), the cross-coupled
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FIGURE 2.23 Different configuration
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In some literature, the authors lik
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Concluding Remarks The feature size
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FIGURE 2.30 Other pass-transistor c
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FIGURE 2.34 Dual-rail, pass-transis
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Shannon expansion, as shown below,
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FIGURE 2.41 Logic circuit for Out =
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FIGURE 2.45 Example decomposed BDD.
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FIGURE 2.50 Diffusion-area sharing
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FIGURE 2.54 Relationship between co
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29. Shiple, T. R., Hojati, R., Sang
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FIGURE 2.58 DTMOS devices: (a) stan
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the design into a BDD representatio
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FIGURE 2.63 Circuit diagram of 2-in
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FIGURE 2.67 Synthesis of 2-input AN
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pass-transistor logic, constrained
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16. Bryant, R., Graph-based algorit
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As shown in Fig. 2.79, the MOSFET d
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n + SiO 2 FIGURE 2.80 No reverse bo
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structures are a powerful solution
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SiO 2 Epitaxial Si Double Layered P
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PD-SOI Application to High-Performa
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LO FIGURE 2.91 History effects. sta
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weight, lower cost, a wireless inte
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TABLE 2.7 Performance of sub-1 V MT
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X X FIGURE 2.99 1 V A/D converter u
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When the communication range is 10
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13. Nakashima, S., et al., Thicknes
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FIGURE 3.1 Conventional ECL circuit
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FIGURE 3.4 FIGURE 3.5 IPULL-DOWN, f
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FIGURE 3.7 V REG voltage regulator
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FIGURE 3.10 Layout of inverter gate
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FIGURE 3.14 Power consumption versu
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I/O Pads, is 60 mW for the multiple
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FIGURE 3.21 Maximum data rate vs. V
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John C. McCallum National Universit
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enchmark took between 15.7 and 5115
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The improvements in line widths, ch
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performance. Extensive lists of SPE
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TABLE 4.6 Functions of Memory and S
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Memory Price ($/MB) FIGURE 4.3 Cost
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TABLE 4.8 Disk Drive Characteristic
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4.6 Summary The performance of comp
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53. Curnow, H. J., and Wichmann, B.
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Computer Systems and Architecture
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advances that have been made in dev
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Server Types Servers are optimized
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Total Cost of Ownership Another con
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edundant memory-bit steering, and s
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majority of the users. Hardware opt
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processor implementations. In later
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FIGURE 5.7 an operand. For larger c
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Example 2 This example contrasts th
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Unlike the Defoe, the IA-64 archite
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This 3-phase approach fails to expl
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6. Scott Rixner, William J. Dally,
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Shared-memory vector architectures
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The vector ISA usually fixes the ma
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FIGURE 5.12 A vector unit construct
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use of loops optimized for certain
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9. Espasa, R., Advanced Vector Arch
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thread management (including run-ti
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In the message passing model, inter
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multithreaded hardware, possibly by
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synchronization) and usually contai
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a common register space, it is impo
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FIGURE 5.16 When a load request is
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list a few helpful references to wh
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38. S. Wallace, B. Calder, and D. T
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Of course, SIMD machines have limit
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computation, it will decrease execu
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FIGURE 5.19 An 8 × 8 baseline netw
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Since then, we have seen constant e
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Stack: Data: Text: Virtual Space FI
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FIGURE 5.25 Shared memory. Shared m
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0 2 GB Text Init. Data Bss Heap . .
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In general, if the necessary transl
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Mark Smotherman Clemson University
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Studies of Instruction-Level Parall
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Modern Designs Most high-performanc
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The principle of register renaming
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FIGURE 6.2 issued into the RS, (ii)
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Issue Dispatch FIGURE 6.4 The proce
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FIGURE 6.6 Scope of register renami
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FIGURE 6.9 State transition diagram
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Thus, the maximal number of instruc
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In addition, if rename buffers are
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it, whose role will be explained su
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As Fig. 6.13 shows, the latest proc
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for recovery. Both processors incor
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FIGURE C. The principle of direct i
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13. White, S. and Reysa, J., PowerP
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FIGURE 6.14 A branch flowing throug
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is that mispredictions limit the pr
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fetch engine to identify which inst
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FIGURE 6.17 A schematic for a bimod
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FIGURE 6.20 A schematic for a GAg g
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FIGURE 6.22 A schematic for a hybri
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time this branch is seen, those bit
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FIGURE 6.23 Branch prediction accur
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11. Price, C., MIPS IV Instruction
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transporting digital bits, whereas
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FIGURE 6.25 The system architecture
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Finally, because the main task of n
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Pradip Bose IBM T. J. Watson Resear
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frequency © 2002 by CRC Press LLC
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The “mips” metric for performan
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Hence, in this paper, we do not dwe
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FIGURE 7.4 Parallel SIMD architectu
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dynamic prediction of such idle mod
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FIGURE 7.7 High-level block-diagram
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(measured as a performance over pow
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Steady-state BIPS FIGURE 7.9 Perfor
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8. Larson, A. G., “Cost-effective
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Jozo J. Dujmović San Francisco Sta
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x FIGURE 8.1 Movement of the disk I
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The critical distance x ∗ is Ther
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Hence, the average seek time for th
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FIGURE 8.4 Four-parameter exponenti
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Access Time [ms] 2 0 0 FIGURE 8.6 M
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TABLE 8.1 Classification of Eight B
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The traditional load independent me
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Response Time [seconds] 290 270 250
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FIGURE 8.14 Prediction errors e(p)
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8.2 Performance Evaluation: Techniq
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is generally the most important mea
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tool to profile programs on the Alp
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driven simulation has two major pro
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A variety of profiling tools exist
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SPLASH, the SPLASH suite was create
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different types and complexity eith
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Table 8.8 provides sources for the
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37. MediaBench benchmarks, http://w
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(a) Instruction Cache (b) Trace Cac
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PE 0 Local Register File local bypa
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The large trace processor instructi
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11. S. Melvin, M. Shebanow, and Y.
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complement numbers. Sign magnitude
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where ak, bk, and ck are the inputs
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k + 1, etc. Extending to a third st
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a 12:15 b 12:15 4-Bit RCA s 12:15 F
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FIGURE 9.8 Two’s complement subtr
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FIGURE 9.10 Unsigned 6 by 6 array m
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Digit Recurrent Division The most c
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FIGURE 9.14 Nonrestoring division.
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The Newton-Raphson division algorit
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FIGURE 9.18 Floating-point addition
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9.2 Fast Adders and Multipliers Gen
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FIGURE 9.20 Half-adder circuit. is
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gi pi FIGURE 9.22(b) Carry skip cir
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Assuming that a single level of the
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Array-Type Multiplier The simplest
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p1i,j p2i,j p3i,j p4i,j FIGURE 9.27
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The partial product PP j is to be c
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The sum SGN of the whole extended b
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FIGURE 9.31 54 × 54-bit parallel m
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6. Svensson, C. and Tjarnstrom, R.,
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Design Techniques III 10 Timing and
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FIGURE 10.1 Typical chip interface.
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Loop Components PLLs and DLLs share
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delay is some fraction of the input
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esults from the voltage across the
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© 2002 by CRC Press LLC TABLE 10.2
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db(H( ω)) FIGURE 10.6 PLL closed-l
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ω )/G O) db(T(ω ph(T( )/π) FIGUR
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Phase Margin (deg) FIGURE 10.10 PLL
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FIGURE 10.11 Measured PLL jitter hi
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FIGURE 10.13 DLL output jitter sens
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FIGURE 10.18 PLL output jitter sens
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Active supply filters employ amplif
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FIGURE 10.19 Single-ended delay ele
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Frequency (GHz) FIGURE 10.23 Freque
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FIGURE 10.25 Push-pull charge pump.
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Circuit Summary In general, all DLL
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5. T. Lee, et al., “A 2.5V CMOS D
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introduced in [3], is used to repre
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FIGURE 10.31 Setup and hold time ti
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FIGURE 10.34 Max-timing diagrams fo
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FIGURE 10.37 Time borrowing for sin
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FIGURE 10.39 Max-timing for single-
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Min-Timing In contrast to max-timin
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FIGURE 10.46 Min-timing diagrams fo
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FIGURE 10.49 Edge-triggered, flip-f
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FIGURE 10.51 FIGURE 10.52 Max-timin
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FIGURE 10.53 Transparent-high latch
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FIGURE 10.57 Complementary TSPC tra
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FIGURE 10.61 A positive, edge-trigg
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FIGURE 10.66 Pulsed latches. FIGURE
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assigned, based on the subcircuit t
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FIGURE 10.71 Scan chain for dual-ph
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and SEB, which are complementary, c
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when used as a flip-flop, the NAND3
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FIGURE 10.75 Integrated memory for
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Memory Cell Bit# Bit FIGURE 10.78 A
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FIGURE 10.80 Modeling process varia
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FIGURE 10.83 A column circuit. FIGU
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advantage of this circuit, compared
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K. Wayne Current University of Cali
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11.2 Nonvolatile Multiple-Valued Me
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Multiple-Valued EEPROM and Flash Me
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direct properly scaled and logicall
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Current comparators are a critical
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FIGURE 11.4 Current-mode CMOS quate
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FIGURE 11.6 Current-mode CMOS quate
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observed with these circuits are ab
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FIGURE 11.11 Current-mode CMOS quat
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FIGURE 11.12 Current-mode CMOS latc
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FIGURE 11.14 Current-mode CMOS anal
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esults confirm the DC transfer func
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19. T. T. Dao, “Threshold I2L and
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FIGURE 12.1 Device technologies use
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selected depending on the number of
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Architecture of Newer Generation FP
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FIGURE 12.6 An example of a simple
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tools, the + 1 operation is a speci
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download the programming data to th
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FIGURE 13.1 High-performance microp
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FIGURE 13.4 TABLE 13.1 combinationa
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accurate wire modelling while limit
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FIGURE 13.6 Dual-rail, multiple-out
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FIGURE 13.10 Effect of output inver
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FIGURE 13.11 Unfooted dynamic 4:1 m
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(4) the limit of designers to manag
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© 2002 by CRC Press LLC IV Design
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FIGURE 14.1 FIGURE 14.2 newer semic
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The cost impact of power consumptio
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In any case, the move toward “Gre
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This static current is obviously un
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14.2 Power Estimation As we saw in
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lock as a function of various param
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FIGURE 14.8 Sleep transistors to co
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FIGURE 14.12 Data enabling. In addi
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Power Reduction through CAD Tools P
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AMPS AMPS is a circuit power optimi
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Masayuki Miyazaki Hitachi, Ltd. 15.
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15.3 Supply Voltage Management Supp
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FIGURE 15.4 MT-CMOS balloon circuit
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FIGURE 15.9 EVT-CMOS circuit design
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FIGURE 15.12 VT-CMOS block diagram
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FIGURE 15.15 SA-V t CMOS scheme wit
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FIGURE 15.17 Floating-point datapat
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Vivek De Intel Corporation Ali Kesh
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FIGURE 16.3 n-channel ID vs. VG sho
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Gate-Induced Drain Leakage (I 4) GI
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FIGURE 16.7 Two-nMOS stack in a two
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FIGURE 16.11 Four-input NAND NMOS s
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FIGURE 16.12 Distribution of standb
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Die count FIGURE 16.16 (a) Adaptive
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due to adaptive body bias worsens w
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Thomas D. Burd University of Califo
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the parameter to be maximized since
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17.3 Dynamically Varying Voltage If
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The voltage converter required for
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FIGURE 17.7 DVS improvement for UI
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FIGURE 17.10 Measured throughput vs
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FIGURE 17.12 Sources of noise margi
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FIGURE 17.15 Ring oscillator adapti
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FIGURE 17.17 Sense-amp delay variat
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Christian Piguet CSEM: Centre Suiss
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Some Basic Rules There are some bas
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TABLE 18.2 Looped 8-bit Multiply Pr
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FIGURE 18.4 new architecture paradi
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FIGURE 18.6 FIGURE 18.7 the second
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FIGURE 18.9 FIGURE 18.10 With latch
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18.6 Low-Power Memories As memories
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connected to the main bitline (only
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is today only about a factor of 2 d
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Katsunori Seno SONY Corporation 19.
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FIGURE 19.2 FIGURE 19.3 This is a c
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FIGURE 19.6 FIGURE 19.7 FPGA sacrif
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FIGURE 19.9 RAWn precharge FIGURE 1
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a fixed given standard. References
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Hendrawan Soeleman Purdue Universit
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for a short period of time when bot
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Total Average Power Putting togethe
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Signal Probability Calculation In c
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Example Given y = x 1 + x 2. Find P
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A B F FIGURE 20.7 Unfactorized and
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etween successive transitions is a
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FIGURE 20.9 A logic gate and its co
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Results Using Probabilistic Techniq
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FIGURE 20.12 Probabilistic and stat
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14. VLSI Microprocessors: A Guide t
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0 V to a voltage V in time T throug
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a small fraction of circuit nodes t
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FIGURE 21.6 (a) E-R latch, (b) timi
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FIGURE 21.8 Potential E-R latch for
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The only difference between the two
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FIGURE 21.14 (a) Clock-powered stat
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FIGURE 21.17 The three drivers used
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FIGURE 21.20 Energy-delay product (
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FIGURE 21.22 AC-1 clock-driver sche
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clock-powered nodes accounted for 8
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Two generations of clock-powered mi
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29. Athas, W. C., Tzartzanis, N., M
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Wayne Wolf Princeton University 22.
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so a CPU could be built in reconfig
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optimized processes. Although embed
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FIGURE 22.4 they are most useful wh
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• How should processes be allocat
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25 memory system activity. If the c
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Jonathan W. Valvano University of T
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FIGURE 23.2 6808, I/O ports A and B
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personnel so that they are proficie
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BDM can provide the ability to obse
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minor modifications to the finite s
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Many components are included in a d
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will saturate resulting in a bang-b
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Two approaches are used to synchron
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Fred J. Taylor University of Florid
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The output of a linear system havin
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FIGURE 24.2 Effects of windowing an
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frequency range ±f Nyquist = ±f s
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performance, complexity, and precis
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overflows of intermediate sums. Tha
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24.7 Applications “DSP” has bec
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DSP (Digital Signal Processor) or D
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PC as a Terminal (Modem) The PC is
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characteristics: • Fixed pictures
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HDTV (and Digital Television) If th
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Modem Banks This is the first of th
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25.7 Automotive, Industrial Althoug
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36. MITEL Semiconductor: www.semico
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26.2 Digital Filters The theory of
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|H | 1 FIGURE 26.1 Frequency select
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TABLE 26.1 Filter Design Comparison
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frequency response involves some al
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Impulse Response 0.2 0.15 0.1 0.05
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This constrained magnitude problem
Page 724 and 725:
FIGURE 26.8 Block diagram of the ge
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11. Ikehara, M., Tanaka, H., and Ku
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Adam Dabrowski Poznan University To
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−12 2 The reference in this case
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FIGURE 27.1 Two-band filter bank: (
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FIGURE 27.5 Critical bandwidth and
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FIGURE 27.7 Threshold of audibility
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FIGURE 27.10 Critical band index in
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FIGURE 27.13 Simplified model of th
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All described masking effects are e
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where From Eqs. (27.32) and (27.33)
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eal-time. Although floating-point p
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FIGURE 27.19 Overlapped, power comp
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FIGURE 27.21 Symmetrical FIR filter
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In the MPEG-1 encoder an efficient
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These first two approaches can be m
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FIGURE 27.27 ADPCM: (a) encoder, (b
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FIGURE 27.30 General scheme of the
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A new standard MPEG-7, named the mu
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FIGURE 27.32 AC-3 encoder. FIGURE 2
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29. Marven, C. and Ewers, G., A sim
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FIGURE 28.1 FIGURE 28.2 of the huma
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FIGURE 28.5 image of reasonable qua
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Consider the case of a simple 2-D
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FIGURE 28.8 A sampled spatiotempora
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oth high spatial frequency componen
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If it were separable (that is, H(ξ
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The first hypothesis (the less plau
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FIGURE 28.17 The real (top) and ima
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FIGURE 28.18 The resolution of a ti
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FIGURE 28.20 Motion estimation by b
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where the superscripts n and n + 1
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must be found via a finite set of o
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The methods in this subsection are
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FIGURE 28.28 The split phase. Using
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35. Y.-T. Wu, T. Kanade, J. Cohn, a
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29.2 Power Dissipation in Digital C
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additional power benefits. Efficien
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Power savings are higher than simpl
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A priori knowledge of data stream d
Page 804 and 805:
FIGURE 29.7 DCT Chip [my DCT] avera
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19. S. Uramoto et al. A 100 MHz 2-D
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Anna Hać University of Hawaii 30.1
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Using TCP/IP also makes the network
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this is for one mobile host which i
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TDMA Time-division multiple access
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path delay. The two measures of the
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are under the control of the system
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the bandwidth reserved in the targe
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Chik-Kong Ken Yang University of Ca
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FIGURE 31.3 attenuation (dB) -3.0 -
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FIGURE 31.5 to handle large voltage
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p_o V bias FIGURE 31.8 High-impedan
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out[n] a0 FIGURE 31.11 Transmitter
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FIGURE 31.15 Receiver design using
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digitally by first feeding the inpu
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FIGURE 31.19 90 o locking using XOR
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Power of these links is becoming an
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46. Takahashi, T. et al., “A CMOS
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Many computer programs exhibit some
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32.3 Related Memory Models, Hierarc
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32.6 External Sorting and Related P
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uns, each striped across the disks.
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Matrix transposition is the special
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of satellite images is over one ter
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TABLE 32.3 Best Known I/O Bounds fo
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e accessed directly in a single I/O
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implementations of priority queues
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By the reduction in [52], a data st
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set of experiments on various text
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3. A. Acharya, M. Uysal, and J. Sal
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44. J. L. Bentley and J. B. Saxe. D
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85. P. Ferragina and F. Luccio. Dyn
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128. V. Kumar and E. Schwabe. Impro
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173. H. Samet. The Design and Analy
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Peter J. Varman Rice University 33.
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need for fast transfers, up to 1 Gb
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In contrast to prefetching that mas
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The ERF algorithm was analyzed in [
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increment lowestPriorityOnDisk(disk
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External or out-of-core algorithms
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33. M. Kallahalla and P. J. Varman.
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advanced, reaching the point where
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FIGURE 34.3 FIGURE 34.4 defines the
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FIGURE 34.5 Trellis representation
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FIGURE 34.7 Data and servo sectors.
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References 1. S. H. Charrap, P. L.
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High-frequency noise is then remove
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The various components of (34.3) ar
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sequences are more complex, and occ
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ange of 10 −12 -10 −15 . Anothe
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strategies are reviewed, which have
Page 908 and 909:
FIGURE 34.13 Magnitude response for
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taps as an approximation of the Typ
Page 912 and 913:
FIGURE 34.18 CTF BER performance de
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TABLE 34.1 Examples of Equalizers I
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(i) Symbol rate VCO based timing re
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Symbol Rate Timing Recovery Schemes
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where B = B 1 + B 2 + 1 is the size
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in � out � d(k) FIGURE 34.26 Ti
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�(k) z -L FIGURE 34.27 Linearized
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10 −4 and 10 −2 . The steady-st
Page 928 and 929:
16. J. Sonntag, et al., “A high s
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y a special sequence known as the a
Page 932 and 933:
FIGURE 34.36 An example of two Gray
Page 934 and 935:
track n track n +1 FIGURE 34.38 The
Page 936 and 937:
FIGURE 34.40 The phase format. Posi
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increase linear density while mitig
Page 940 and 941:
FIGURE 34.43 Two equivalent constra
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FIGURE 34.44 Possible pairs of sequ
Page 944 and 945:
Distance δ min,C can be bounded as
Page 946 and 947:
FIGURE 34.45 Standard concatenation
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30. E. Soljanin, “On-track and of
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The next block is PRE. What is PR?
Page 952 and 953:
In the above example, we see that s
Page 954 and 955:
FIGURE 34.52 Feedback detector. Cat
Page 956 and 957:
FIGURE 34.54 Decision feedback equa
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esponse (this is also valid for dif
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its use, while the computational, o
Page 962 and 963:
FIGURE 34.58 Viterbi algorithm dete
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Noise-Predictive Maximum Likelihood
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The error event likelihoods are cal
Page 968 and 969:
Metric-First Algorithms Metric-firs
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termination at the same time. Howev
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13. J. Hagenauer, “Applications o
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Given a code C of length n and dime
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Notice that, although a code may ha
Page 978 and 979:
Not many perfect codes exist. In th
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Theorem 1 (Shannon) For any � > 0
Page 982 and 983:
If we write the codewords as polyno
Page 984 and 985:
Let © 2002 by CRC Press LLC 1 1 K
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which, in polynomial form, is Let E
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m Σl=0 because a l = (a m+1 − 1)
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Evaluating the syndromes, we obtain
Page 992 and 993:
FIGURE 34.63 Interleaving m times o
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Operating System © 2002 by CRC Pre
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systems, the most notable example b
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35.3 Components of Distributed Oper
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Message-passing IPC shares many cha
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process at any arbitrary stage in i
Page 1004 and 1005:
Few attempts were made in the past
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alternate years with SOSP), and the
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© 2002 by CRC Press LLC 42 Mobile
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FIGURE 36.1 were introduced; howeve
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FPGAs by providing on-chip memory f
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FIGURE 36.3 There is a considerable
Page 1016 and 1017:
FIGURE 36.4 The general form of the
Page 1018 and 1019:
FIGURE 36.6 If a VPB can cover all
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John Morris University of Western A
Page 1022 and 1023:
FIGURE 37.1 Simplified block diagra
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inverters, and the programmable mul
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Programmable Active Memories (PAM)
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measured in megabits, not megabytes
Page 1030 and 1031:
from modern FPGAs, e.g., Altera’s
Page 1032 and 1033:
Regularity A processing pipeline wi
Page 1034 and 1035:
Other Languages Other routes from h
Page 1036 and 1037:
29. Bergmann, N.W., and Dawood, A.,
Page 1038 and 1039:
FIGURE 37.8 Basic architectures for
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Selected Applications Several class
Page 1042 and 1043:
Development Systems Developing appl
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implementation. Reconfigurable or r
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The designer starts the generation
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lock but allows multiple instructio
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data-types that are mapped to TIE r
Page 1052 and 1053:
Conclusions Configurable and extens
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FIGURE 38.1 Average vehicle speed.
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AHS means advanced cruise-assist hi
Page 1058 and 1059:
TABLE 38.3 navigation systems VICS
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FIGURE 38.9 FIGURE 38.10 a map data
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FIGURE 38.13 The four basic functio
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Support of Safe Driving System The
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System evaluation, the fourth issue
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FIGURE 38.18 Summarizing the three
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uilder tools serving to build MMI o
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FIGURE 38.23 FIGURE 38.24 automaker
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FIGURE 38.26 FIGURE 38.27 The first
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FIGURE 38.30 FIGURE 38.31 a kind of
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To Probe Further In this field, the
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FIGURE 39.1 of more versatile media
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1 • 3DNow! 1 [11,12] from AMD,
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FIGURE 39.6 In the packed add instr
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If saturation arithmetic is used in
Page 1088 and 1089:
TABLE 39.1 Examples of Operations T
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FIGURE 39.10 PAVG R c, R a, R b: Pa
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FIGURE 39.13 Packed compare instruc
Page 1094 and 1095:
FIGURE 39.17 Packed multiply high i
Page 1096 and 1097: FIGURE 39.20 Packed multiply left i
Page 1098 and 1099: FIGURE 39.23 In the packed multiply
Page 1100 and 1101: TABLE 39.4 Packed Integer Multiplic
Page 1102 and 1103: Subword Permutation Instructions In
Page 1104 and 1105: is n log 2(n). Table 39.6 shows how
Page 1106 and 1107: FIGURE 39.33 Mix left instruction.
Page 1108 and 1109: TABLE 39.7 Subword Permutation Inst
Page 1110 and 1111: TABLE 39.9 IA-64 uses FPMA and FPMS
Page 1112 and 1113: The two-bit result field is written
Page 1114 and 1115: source register. These three FP mix
Page 1116 and 1117: 13. Motorola, “AltiVec technology
Page 1118 and 1119: FIGURE 39.48 ManArray 1 × 2 core e
Page 1120 and 1121: FIGURE 39.50 Hypercube interconnect
Page 1122 and 1123: FIGURE 39.53 Host DSP software laye
Page 1124 and 1125: Benchmark Data Type Performance 256
Page 1126 and 1127: FIGURE 39.56 Simplified schematic o
Page 1128 and 1129: Gaming applications often require a
Page 1130 and 1131: system, and the application program
Page 1132 and 1133: FIGURE 39.60 FIGURE 39.61 FIGURE 39
Page 1134 and 1135: FIGURE 39.64 shifting place limits
Page 1136 and 1137: FIGURE 39.66 pitch Address Looping
Page 1138 and 1139: FIGURE 39.69 gain of the filter. It
Page 1140 and 1141: as a core to integrate onto the sam
Page 1142 and 1143: 7. Rossum, D., Constraint based aud
Page 1144 and 1145: FIGURE 39.74 Procedure for simulate
Page 1148 and 1149: FIGURE 39.77 The tabu list can be v
Page 1150 and 1151: Intermediate-term memory component
Page 1152 and 1153: FIGURE 39.81 Selection. FIGURE 39.8
Page 1154 and 1155: Work on parallelization of the tabu
Page 1156 and 1157: Acknowledgment The authors acknowle
Page 1158 and 1159: 47. L. K. Grover. A new Simulated A
Page 1160 and 1161: 93. E. M. Rudnick, J. H. Patel, G.
Page 1162 and 1163: 138. Youn-Long Lin, Yu-Chin Hsu, an
Page 1164 and 1165: This architecture allowed for large
Page 1166 and 1167: FIGURE 40.3 40.3 Application Server
Page 1168 and 1169: FIGURE 40.5 applications. CORBA cur
Page 1170 and 1171: FIGURE 40.8 The application server
Page 1172 and 1173: FIGURE 40.10 The proposed architect
Page 1174 and 1175: cost at the front end. The ideal ap
Page 1176 and 1177: FIGURE 40.12 Client-server and netw
Page 1178 and 1179: Yoshiaki Hagiwara Sony Corporation
Page 1180 and 1181: FIGURE 41.2 This corresponds to the
Page 1182 and 1183: FIGURE 41.5 Buried channel CCD stru
Page 1184 and 1185: FIGURE 41.8 Applications of CCD and
Page 1186 and 1187: sensor (1100 K pixel) Microphone ×
Page 1188 and 1189: �� FIGURE 41.14 Form com
Page 1190 and 1191: FIGURE 41.16 Stack technology. Link
Page 1192 and 1193: 1st EmDRAM Product FIGURE 41.19 Emb
Page 1194 and 1195: FIGURE 41.23 Em-DRAM process techno
Page 1196 and 1197:
John F. Alexander University of Nor
Page 1198 and 1199:
Several reasons exist for mentionin
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sort of local area network. Support
Page 1202 and 1203:
Technological hurdles must be overc
Page 1204 and 1205:
FIGURE 42.1 Block diagram of (a) ge
Page 1206 and 1207:
FIGURE 42.3 Alternative FIR filter
Page 1208 and 1209:
FIGURE 42.5 Polyphase filter struct
Page 1210 and 1211:
3. E. Dahlman, Bjorn Gudmundson, M.
Page 1212 and 1213:
TABLE 42.1 Categorizing Reusable Co
Page 1214 and 1215:
FIGURE 42.11 Internet growth. FIGUR
Page 1216 and 1217:
FIGURE 42.13 Software for VoIP SoC.
Page 1218 and 1219:
FIGURE 42.17 Bandwidth trends. FIGU
Page 1220 and 1221:
The interconnections between the va
Page 1222 and 1223:
FIGURE 42.21 A typical communicatio
Page 1224 and 1225:
carry information over longer dista
Page 1226 and 1227:
however, it is not uncommon that so
Page 1228 and 1229:
node, to which the user is connecte
Page 1230 and 1231:
its information. Intuitively, this
Page 1232 and 1233:
This issue is referred to as latenc
Page 1234 and 1235:
Evolution of Standard Image/Video C
Page 1236 and 1237:
FIGURE 42.26 Sequence of zigzag-cod
Page 1238 and 1239:
FIGURE 42.29 150th Frame of origina
Page 1240 and 1241:
FIGURE 42.31 Data partitioning for
Page 1242 and 1243:
for transmitting a predictively cod
Page 1244 and 1245:
42.6 Pen-Based User Interfaces—An
Page 1246 and 1247:
FIGURE 42.35 The handwriting recogn
Page 1248 and 1249:
• No toggling between edit/contro
Page 1250 and 1251:
FIGURE 42.39 Boxplots of time to en
Page 1252 and 1253:
0 … 9 ( ) -
Page 1254 and 1255:
is using the SMIL generic media ref
Page 1256 and 1257:
FIGURE 42.47 Example of pen-and-pap
Page 1258 and 1259:
slots specifying pieces of informat
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most stringent demands for low-powe
Page 1262 and 1263:
FIGURE 42.51 MIPS requirement of se
Page 1264 and 1265:
FIGURE 42.54 von Neumann architectu
Page 1266 and 1267:
FIGURE 42.57 Dual Mac architecture
Page 1268 and 1269:
FIGURE 42.59 Two data path variatio
Page 1270 and 1271:
FIGURE 42.60 Basic pipeline archite
Page 1272 and 1273:
References 1. Bahl L., Cocke J., Je
Page 1274 and 1275:
Random Oracle Model One direction o
Page 1276 and 1277:
at the end of its usefulness. A new
Page 1278 and 1279:
function and verification function
Page 1280 and 1281:
underlies the Diffie-Hellman key ag
Page 1282 and 1283:
31. Dolev, D., Dwork, C., and Naor,
Page 1284 and 1285:
R. Chandramouli Synopsys Inc. 44.1
Page 1286 and 1287:
many deficiencies in the design may
Page 1288 and 1289:
impact the performance and test ove
Page 1290 and 1291:
FIGURE 44.4 FIGURE 44.5 It becomes
Page 1292 and 1293:
SoC methodologies require synchroni
Page 1294 and 1295:
This method, however, lacks the app
Page 1296 and 1297:
FIGURE 45.2 Input formats of MILEF
Page 1298 and 1299:
FIGURE 45.3 FIGURE 45.4 Robustness
Page 1300 and 1301:
FIGURE 45.5 In Fig. 45.5 a simple e
Page 1302 and 1303:
difficult to identify for overcurre
Page 1304 and 1305:
General Approach in Comparison with
Page 1306 and 1307:
FIGURE 45.10 phase—the propagatio
Page 1308 and 1309:
shorter test sequence could possibl
Page 1310 and 1311:
Looking at results of Table 45.6, i
Page 1312 and 1313:
45.4 Summary Automatic test pattern
Page 1314 and 1315:
41. F. Brglez, H. Fujiwara: A neutr
Page 1316 and 1317:
FIGURE 46.1 Rule of ten in testing
Page 1318 and 1319:
FIGURE 46.5 FIGURE 46.6 of the DUT.
Page 1320 and 1321:
FIGURE 46.7 generate tests for ever
Page 1322 and 1323:
FIGURE 46.9 FIGURE 46.10 behavioral
Page 1324 and 1325:
Z. Stamenković University of N. St
Page 1326 and 1327:
FIGURE 47.2 Murphy’s Model The mo
Page 1328 and 1329:
where k is the total number of crit
Page 1330 and 1331:
FIGURE 47.5 Definition of IC critic
Page 1332 and 1333:
distribution [34,35]: © 2002 by CR
Page 1334 and 1335:
and, in the case of w = X 2 − X 1
Page 1336 and 1337:
Algorithm • Input: a singly linke
Page 1338 and 1339:
TRACIF takes a CIF file as input an
Page 1340 and 1341:
TABLE 47.2 Critical Area Extraction
Page 1342 and 1343:
Also, the critical areas for lithog
Page 1344 and 1345:
(enhancement of the process cleanli
Page 1346 and 1347:
28. Corsi, F. and Martino, S., Defe
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U. Glaeser

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