U. Glaeser

FIGURE 29.7 DCT Chip [my DCT] average power vs. compressed image quality.
FIGURE 29.8 Compressed image quality and power.
Amirtharajah et al. [34,36] use a similar DA-based approximate processing technique in a programmable
ultra-low-power DSP targeted to physiological monitoring. Reduced RAC iterations reduce the
signal to noise ratio of the input signal (effectively increasing the quantization noise) and result in less
reliable heart beat detection. Yet, the reduced performance results in linear power savings, which may be
desirable in certain situations.
29.5 Conclusion
This chapter has presented a collection of power reduction techniques that applied in DSP applications
during the last few years. Although presented in a categorized form to provide structure in the exposition,
the author does not believe that true low-power design involves a laundry list of power reduction
techniques and blind application to the problem in question. Instead, low-power design involves a vertical
design process and a global optimization across algorithmic, architectural, circuit, and physical design
boundaries. A designer must have a deep understanding of the DSP application under power optimization.
The best algorithm must be selected, which minimizes a weighted average of the number of
arithmetic operations, memory accesses, on-chip communication, and silicon area. The right boundary
must be achieved between programmability and predefined functionality. Architectural, circuit, and
physical design techniques must then be applied that fully support the algorithmic selection but at the
same time should be allowed to influence such selection in order to achieve optimum results.
The author concludes by enumerating a few case studies, which demonstrate concurrent optimization
across all design phases and as a result have achieved impressive results in a few key DSP application areas.
Chandrakasan et al. [35] have demonstrated a low-power chipset for a portable multimedia terminal.
This work was power optimized from a system perspective and performed a number of functions such as
protocol conversion, synchronization, and video decompression, among others, while consuming under
5 mW of power. Amirtharajah [36] has demonstrated an ultra-low-power programmable DSP for physiological
monitoring (heartbeat detection and classification). His techniques involved algorithmic design,
© 2002 by CRC Press LLC
Power (mW)
5.5
5
4.5
4
3.5
3
20 25 30 35
PSNR (dB)
40 45 50
23.99 dB 3.07 mW 32.46 dB 4.30 mW 44.84 dB 5.11 mW