Exploiting Errors for Efficiency: A Survey from Circuits to Algorithms <s> Embedded, digital signal processing, and multimedia systems <s> In this paper, we propose a framework for low-energy digital signal processing (DSP) where the supply voltage is scaled beyond the critical voltage required to match the critical path delay to the throughput. This deliberate introduction of input-dependent errors leads to degradation in the algorithmic performance, which is compensated for via algorithmic noise-tolerance (ANT) schemes. The resulting setup comprised of the DSP architecture operating at sub-critical voltage and the error control scheme is referred to as soft DSP. It is shown that technology scaling renders the proposed scheme more effective as the delay penalty suffered due to voltage scaling reduces due to short channel effects. The effectiveness of the proposed scheme is also enhanced when arithmetic units with a higher "delay-imbalance" are employed. A prediction based error-control scheme is proposed to enhance the performance of the filtering algorithm in presence of errors due to soft computations. For a frequency selective filter, it is shown that the proposed scheme provides 60%-81% reduction in energy dissipation for filter bandwidths up to 0.5 /spl pi/ (where 2 /spl pi/ corresponds to the sampling frequency f/sub s/) over that achieved via conventional voltage scaling, with a maximum of 0.5 dB degradation in the output signal-to-noise ratio (SNR/sub o/). It is also shown that the proposed algorithmic noise-tolerance schemes can be used to improve the performance of DSP algorithms in presence of bit-error rates of up to 10/sup -3/ due to deep submicron (DSM) noise. <s> BIB001 </s> Exploiting Errors for Efficiency: A Survey from Circuits to Algorithms <s> Embedded, digital signal processing, and multimedia systems <s> This paper provides an overview of algorithmic noise-tolerance (ANT) for designing reliable and energy-efficient digital signal processing systems. Techniques such as prediction-based, error cancellation-based, and reduced precision redundancy based ANT are discussed. Average energy-savings range from 67% to 71% over conventional systems. Fluid IP core generators are proposed as a means of encapsulating the benefits of an ANT-based low-power design methodology. CAD issues resident in such a methodology are also discussed. <s> BIB002 </s> Exploiting Errors for Efficiency: A Survey from Circuits to Algorithms <s> Embedded, digital signal processing, and multimedia systems <s> A recent trend in low-power design has been the employment of reduced precision processing methods for decreasing arithmetic activity and average power dissipation. Such designs can trade off power and arithmetic precision as system requirements change. This work explores the potential of distributed arithmetic (DA) computation structures for low-power precision-on-demand computation. We present an ultralow-power DSP which uses variable precision arithmetic, low-voltage circuits, and conditional clocks to implement a biomedical detection and classification algorithm using only 560 nW. Low energy consumption enables self-powered operation using ambient mechanical vibrations, converted to electric energy by a MEMS transducer and accompanying power electronics. The MEMS energy scavenging system is estimated to deliver 4.3 to 5.6 /spl mu/W of power to the DSP load. <s> BIB003 </s> Exploiting Errors for Efficiency: A Survey from Circuits to Algorithms <s> Embedded, digital signal processing, and multimedia systems <s> Embedded sensor platforms can dissipate most of their energy in accessing sensor integrated circuits such as gyroscopes. But the algorithms which process the sensor data and the humans who consume the overall output of the system may often be able to tolerate some amount of error in the retrieved sensor values. Because devices are accessed through interfaces provided by system software, exploiting the tolerable error for improvements in energy efficiency requires appropriate system software and hardware support. However, no such support currently exists. ::: ::: We present Lax, a device driver abstraction for interacting with sensors that enables power savings in exchange for occasionally returning erroneous sensor data. Our implementation on a hardware prototype delivers savings in sensor dynamic power dissipation of up to 48% (as compared to precise device access) while providing sensor access error rates lower than 5 data acquisition errors per 100 data accesses. Given the significant proportion of system energy budgets in wearable platforms that are devoted to sensors, approximate sensor data acquisition using Lax can deliver significant system-level energy savings. <s> BIB004
Many computing systems that interact with the physical world or which process data gathered from it, have high computational demands under tightly-constrained resources. These systems, which include many embedded and multi-media systems, must often process noisy inputs and must trade fidelity of their outputs for lower resource usage. Because they are designed to process data from noisy inputs, such as from sensors that convert from an analog signal into a digital representation, these applications are often designed to be resilient to errors or noise in their inputs BIB004 . Several pioneering research efforts investigated trading precision and accuracy for signal processing performance BIB003 and exploiting the tolerance of signal processing algorithms to noise BIB001 BIB002 . When the outputs of such systems are destined for human consumption (e.g., audio and video), common use cases can often tolerate some amount of noise in their I/O interfaces [178-180, 182, 184] .