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7. Rossum, D., Constraint based audio interpolators, IEEE ASSP Workshop on Applications of Signal Processing to Audio and Acoustics (Mohonk), 1993. 8. Zhang, M., Tan, K. C., and Er, M. H., Three-dimensional synthesis based on head-related transfer functions, Journal of the Audio Engineering Society, 46, 836, 1998. 9. Audio Codec ’97 Revision 2.2, Intel Corporation, San Jose, CA, 2000. 10. IEC-958 Digital Audio Interface, International Electrotechnical Commission, 1989. 39.4 Modern Approximation Iterative Algorithms and Their Applications in Computer Engineering Sadiq M. Sait and Habib Youssef Introduction This chapter section discusses one class of combinatorial optimization algorithms: approximation iterative algorithms. We shall limit ourselves to four of these algorithms, which are, in order of their popularity among the engineering community: (1) simulated annealing (SA), (2) genetic algorithm (GA), (3) tabu search (TS), and (4) simulated evolution (SimE). GA and SimE are evolutionary algorithms, a term used to refer to any probabilistic algorithm whose design is inspired by evolutionary mechanisms found in biological species. Evolutionary algorithms, SA and TS have been found very effective and robust in solving numerous problems from a wide range of application domains. Furthermore, they are even suitable for ill-posed problems where some of the parameters are not known beforehand. These properties are lacking in all traditional optimization techniques. The four algorithms share the following properties: 1. They are approximation algorithms, i.e., they do not guarantee finding an optimal solution. Actually, they are blind, in that they do not know when they reached an optimal solution. Therefore, they must be told when to stop. 2. They are neighborhood search algorithms, which start from one suboptimal solution (or a population of solutions) and perform a partial search of the solution space for better solutions. 3. They are all “general.” They are not problem-specific and, practically, they can be tailored to solve any combinatorial optimization problem. 4. They all strive to exploit domain specific heuristic knowledge to bias the search toward “good” solution subspace. The quality of subspace searched depends to a large extent on the amount of heuristic knowledge used. 5. They are easy to implement. All that is required is to have a suitable solution representation, a cost function, and a mechanism to traverse the search space. 6. They have hill climbing property, i.e., they occasionally accept uphill (bad) moves. The goal in this chapter section is to briefly introduce these four powerful algorithms. It is organized into nine sections. In the next four subsections, an intuitive discussion of each of the four iterative algorithms is provided. The remaining sections briefly address convergence aspects of the heuristics, their parallel implementation, and examples of applications. The final subsection concludes the chapter section with a comparison among the heuristics and a glimpse at the notion of hybrids. This chapter section does not provide a full account of any of this important class of heuristics. For more details, readers should consult the numerous references cited in the body of this work. Simulated Annealing Simulated Annealing (SA) is one of the most well-developed and widely used iterative techniques for solving optimization problems. It is a general adaptive heuristic and belongs to the class of nondeterministic algorithms [1]. It has been applied to several combinatorial optimization problems from various fields © 2002 by CRC Press LLC
FIGURE 39.73 Local vs. global optima. of science and engineering. The term annealing refers to heating a solid to a very high temperature (whereby the atoms gain enough energy to break the chemical bonds and become free to move), and then slowly cooling the molten material in a controlled manner until it crystallizes. By cooling the metal at a proper rate, atoms will have an increased chance to regain proper crystal structure with perfect lattices. During this annealing procedure, the free energy of the solid is minimized. In the early 1980s, a correspondence between annealing and combinatorial optimization was estab- ∨ lished, first by Kirkpatrick, Gelatt and Vecchi [2] in 1983, and independently by Cerny [3] in 1985. These scientists observed that a solution in combinatorial optimization is equivalent to a state in the physical system and the cost of the solution is analogous to the energy of that state. As a result of this analogy, they introduced a solution method in the field of combinatorial optimization. This method is thus based on the simulation of the physical annealing process, and hence the name simulated annealing [2,3]. Every combinatorial optimization problem may be discussed in terms of a state space. A state is simply a configuration of the combinatorial objects involved. For example, consider the problem of partitioning a graph of 2n nodes into two equal sized subgraphs such that the number of edges with vertices in both subgraphs is minimized. In this problem, any division of 2n nodes into two equal sized blocks is a configuration. A large number of such configurations exists. Only some of these correspond to global optima, i.e., states with optimum cost. An iterative improvement scheme starts with some given state, and examines a local neighborhood of the state for better solutions. A local neighborhood of a state S, denoted by N(S), is the set of all states which can be reached from S by making a small change to S. For instance, if S represents a two-way partition of a graph, the set of all partitions which are generated by swapping two nodes across the partition represents a local neighborhood. The iterative improvement algorithm moves from the current state to a state in the local neighborhood if the latter has a better cost. If all the local neighbors have larger costs, the algorithm is said to have converged to a local optimum. This is illustrated in Fig. 39.73. Here, the states are shown along the x-axis, and it is assumed that two consecutive states are local neighbors. It is further assumed that we are discussing a minimization problem. The cost curve is nonconvex, i.e., it has multiple minima. A greedy iterative improvement algorithm may start off with an initial solution such as S in Fig. 39.73, then slide along the curve and find a local minimum such as L. There is no way such an algorithm can find the global minimum G of Fig. 39.73, unless it “climbs the hill” at the local minimum L. In other words, an algorithm that occasionally accepts inferior solutions can escape from getting trapped in a local optimum. SA is such a hill-climbing algorithm. During annealing, a metal is maintained at a certain temperature T for a pre-computed amount of time, before reducing the temperature in a controlled manner. The atoms have a greater degree of freedom to move at higher temperatures than at lower temperatures. The movement of atoms is analogous to the generation of new neighborhood states in an optimization process. In order to simulate the annealing process, © 2002 by CRC Press LLC Cost X S X L States X G
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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
Page 714 and 715:
|H | 1 FIGURE 26.1 Frequency select
Page 716 and 717:
TABLE 26.1 Filter Design Comparison
Page 718 and 719:
frequency response involves some al
Page 720 and 721:
Impulse Response 0.2 0.15 0.1 0.05
Page 722 and 723:
This constrained magnitude problem
Page 724 and 725:
FIGURE 26.8 Block diagram of the ge
Page 726 and 727:
11. Ikehara, M., Tanaka, H., and Ku
Page 728 and 729:
Adam Dabrowski Poznan University To
Page 730 and 731:
−12 2 The reference in this case
Page 732 and 733:
FIGURE 27.1 Two-band filter bank: (
Page 734 and 735:
FIGURE 27.5 Critical bandwidth and
Page 736 and 737:
FIGURE 27.7 Threshold of audibility
Page 738 and 739:
FIGURE 27.10 Critical band index in
Page 740 and 741:
FIGURE 27.13 Simplified model of th
Page 742 and 743:
All described masking effects are e
Page 744 and 745:
where From Eqs. (27.32) and (27.33)
Page 746 and 747:
eal-time. Although floating-point p
Page 748 and 749:
FIGURE 27.19 Overlapped, power comp
Page 750 and 751:
FIGURE 27.21 Symmetrical FIR filter
Page 752 and 753:
In the MPEG-1 encoder an efficient
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These first two approaches can be m
Page 756 and 757:
FIGURE 27.27 ADPCM: (a) encoder, (b
Page 758 and 759:
FIGURE 27.30 General scheme of the
Page 760 and 761:
A new standard MPEG-7, named the mu
Page 762 and 763:
FIGURE 27.32 AC-3 encoder. FIGURE 2
Page 764 and 765:
29. Marven, C. and Ewers, G., A sim
Page 766 and 767:
FIGURE 28.1 FIGURE 28.2 of the huma
Page 768 and 769:
FIGURE 28.5 image of reasonable qua
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Consider the case of a simple 2-D
Page 772 and 773:
FIGURE 28.8 A sampled spatiotempora
Page 774 and 775:
oth high spatial frequency componen
Page 776 and 777:
If it were separable (that is, H(ξ
Page 778 and 779:
The first hypothesis (the less plau
Page 780 and 781:
FIGURE 28.17 The real (top) and ima
Page 782 and 783:
FIGURE 28.18 The resolution of a ti
Page 784 and 785:
FIGURE 28.20 Motion estimation by b
Page 786 and 787:
where the superscripts n and n + 1
Page 788 and 789:
must be found via a finite set of o
Page 790 and 791:
The methods in this subsection are
Page 792 and 793:
FIGURE 28.28 The split phase. Using
Page 794 and 795:
35. Y.-T. Wu, T. Kanade, J. Cohn, a
Page 796 and 797:
29.2 Power Dissipation in Digital C
Page 798 and 799:
additional power benefits. Efficien
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Power savings are higher than simpl
Page 802 and 803:
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
Page 808 and 809:
Anna Hać University of Hawaii 30.1
Page 810 and 811:
Using TCP/IP also makes the network
Page 812 and 813:
this is for one mobile host which i
Page 814 and 815:
TDMA Time-division multiple access
Page 816 and 817:
path delay. The two measures of the
Page 818 and 819:
are under the control of the system
Page 820 and 821:
the bandwidth reserved in the targe
Page 822 and 823:
Chik-Kong Ken Yang University of Ca
Page 824 and 825:
FIGURE 31.3 attenuation (dB) -3.0 -
Page 826 and 827:
FIGURE 31.5 to handle large voltage
Page 828 and 829:
p_o V bias FIGURE 31.8 High-impedan
Page 830 and 831:
out[n] a0 FIGURE 31.11 Transmitter
Page 832 and 833:
FIGURE 31.15 Receiver design using
Page 834 and 835:
digitally by first feeding the inpu
Page 836 and 837:
FIGURE 31.19 90 o locking using XOR
Page 838 and 839:
Power of these links is becoming an
Page 840 and 841:
46. Takahashi, T. et al., “A CMOS
Page 842 and 843:
Many computer programs exhibit some
Page 844 and 845:
32.3 Related Memory Models, Hierarc
Page 846 and 847:
32.6 External Sorting and Related P
Page 848 and 849:
uns, each striped across the disks.
Page 850 and 851:
Matrix transposition is the special
Page 852 and 853:
of satellite images is over one ter
Page 854 and 855:
TABLE 32.3 Best Known I/O Bounds fo
Page 856 and 857:
e accessed directly in a single I/O
Page 858 and 859:
implementations of priority queues
Page 860 and 861:
By the reduction in [52], a data st
Page 862 and 863:
set of experiments on various text
Page 864 and 865:
3. A. Acharya, M. Uysal, and J. Sal
Page 866 and 867:
44. J. L. Bentley and J. B. Saxe. D
Page 868 and 869:
85. P. Ferragina and F. Luccio. Dyn
Page 870 and 871:
128. V. Kumar and E. Schwabe. Impro
Page 872 and 873:
173. H. Samet. The Design and Analy
Page 874 and 875:
Peter J. Varman Rice University 33.
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need for fast transfers, up to 1 Gb
Page 878 and 879:
In contrast to prefetching that mas
Page 880 and 881:
The ERF algorithm was analyzed in [
Page 882 and 883:
increment lowestPriorityOnDisk(disk
Page 884 and 885:
External or out-of-core algorithms
Page 886 and 887:
33. M. Kallahalla and P. J. Varman.
Page 888 and 889:
advanced, reaching the point where
Page 890 and 891:
FIGURE 34.3 FIGURE 34.4 defines the
Page 892 and 893:
FIGURE 34.5 Trellis representation
Page 894 and 895:
FIGURE 34.7 Data and servo sectors.
Page 896 and 897:
References 1. S. H. Charrap, P. L.
Page 898 and 899:
High-frequency noise is then remove
Page 900 and 901:
The various components of (34.3) ar
Page 902 and 903:
sequences are more complex, and occ
Page 904 and 905:
ange of 10 −12 -10 −15 . Anothe
Page 906 and 907:
strategies are reviewed, which have
Page 908 and 909:
FIGURE 34.13 Magnitude response for
Page 910 and 911:
taps as an approximation of the Typ
Page 912 and 913:
FIGURE 34.18 CTF BER performance de
Page 914 and 915:
TABLE 34.1 Examples of Equalizers I
Page 916 and 917:
(i) Symbol rate VCO based timing re
Page 918 and 919:
Symbol Rate Timing Recovery Schemes
Page 920 and 921:
where B = B 1 + B 2 + 1 is the size
Page 922 and 923:
in � out � d(k) FIGURE 34.26 Ti
Page 924 and 925:
�(k) z -L FIGURE 34.27 Linearized
Page 926 and 927:
10 −4 and 10 −2 . The steady-st
Page 928 and 929:
16. J. Sonntag, et al., “A high s
Page 930 and 931:
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
Page 938 and 939:
increase linear density while mitig
Page 940 and 941:
FIGURE 34.43 Two equivalent constra
Page 942 and 943:
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
Page 948 and 949:
30. E. Soljanin, “On-track and of
Page 950 and 951:
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
Page 958 and 959:
esponse (this is also valid for dif
Page 960 and 961:
its use, while the computational, o
Page 962 and 963:
FIGURE 34.58 Viterbi algorithm dete
Page 964 and 965:
Noise-Predictive Maximum Likelihood
Page 966 and 967:
The error event likelihoods are cal
Page 968 and 969:
Metric-First Algorithms Metric-firs
Page 970 and 971:
termination at the same time. Howev
Page 972 and 973:
13. J. Hagenauer, “Applications o
Page 974 and 975:
Given a code C of length n and dime
Page 976 and 977:
Notice that, although a code may ha
Page 978 and 979:
Not many perfect codes exist. In th
Page 980 and 981:
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
Page 986 and 987:
which, in polynomial form, is Let E
Page 988 and 989:
m Σl=0 because a l = (a m+1 − 1)
Page 990 and 991:
Evaluating the syndromes, we obtain
Page 992 and 993:
FIGURE 34.63 Interleaving m times o
Page 994 and 995:
Operating System © 2002 by CRC Pre
Page 996 and 997:
systems, the most notable example b
Page 998 and 999:
35.3 Components of Distributed Oper
Page 1000 and 1001:
Message-passing IPC shares many cha
Page 1002 and 1003:
process at any arbitrary stage in i
Page 1004 and 1005:
Few attempts were made in the past
Page 1006 and 1007:
alternate years with SOSP), and the
Page 1008 and 1009:
© 2002 by CRC Press LLC 42 Mobile
Page 1010 and 1011:
FIGURE 36.1 were introduced; howeve
Page 1012 and 1013:
FPGAs by providing on-chip memory f
Page 1014 and 1015:
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
Page 1020 and 1021:
John Morris University of Western A
Page 1022 and 1023:
FIGURE 37.1 Simplified block diagra
Page 1024 and 1025:
inverters, and the programmable mul
Page 1026 and 1027:
Programmable Active Memories (PAM)
Page 1028 and 1029:
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
Page 1040 and 1041:
Selected Applications Several class
Page 1042 and 1043:
Development Systems Developing appl
Page 1044 and 1045:
implementation. Reconfigurable or r
Page 1046 and 1047:
The designer starts the generation
Page 1048 and 1049:
lock but allows multiple instructio
Page 1050 and 1051:
data-types that are mapped to TIE r
Page 1052 and 1053:
Conclusions Configurable and extens
Page 1054 and 1055:
FIGURE 38.1 Average vehicle speed.
Page 1056 and 1057:
AHS means advanced cruise-assist hi
Page 1058 and 1059:
TABLE 38.3 navigation systems VICS
Page 1060 and 1061:
FIGURE 38.9 FIGURE 38.10 a map data
Page 1062 and 1063:
FIGURE 38.13 The four basic functio
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Support of Safe Driving System The
Page 1066 and 1067:
System evaluation, the fourth issue
Page 1068 and 1069:
FIGURE 38.18 Summarizing the three
Page 1070 and 1071:
uilder tools serving to build MMI o
Page 1072 and 1073:
FIGURE 38.23 FIGURE 38.24 automaker
Page 1074 and 1075:
FIGURE 38.26 FIGURE 38.27 The first
Page 1076 and 1077:
FIGURE 38.30 FIGURE 38.31 a kind of
Page 1078 and 1079:
To Probe Further In this field, the
Page 1080 and 1081:
FIGURE 39.1 of more versatile media
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1 • 3DNow! 1 [11,12] from AMD,
Page 1084 and 1085:
FIGURE 39.6 In the packed add instr
Page 1086 and 1087:
If saturation arithmetic is used in
Page 1088 and 1089:
TABLE 39.1 Examples of Operations T
Page 1090 and 1091:
FIGURE 39.10 PAVG R c, R a, R b: Pa
Page 1092 and 1093: 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 1144 and 1145: FIGURE 39.74 Procedure for simulate
Page 1146 and 1147: The fitness value of an individual
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
Page 1260 and 1261:
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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