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Approximate Signal Processing
, 1997
"... It is increasingly important to structure signal processing algorithms and systems to allow for trading off between the accuracy of results and the utilization of resources in their implementation. In any particular context, there are typically a variety of heuristic approaches to managing these tra ..."
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Cited by 324 (2 self)
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It is increasingly important to structure signal processing algorithms and systems to allow for trading off between the accuracy of results and the utilization of resources in their implementation. In any particular context, there are typically a variety of heuristic approaches to managing these tradeoffs. One of the objectives of this paper is to suggest that there is the potential for developing a more formal approach, including utilizing current research in Computer Science on Approximate Processing and one of its central concepts, Incremental Refinement. Toward this end, we first summarize a number of ideas and approaches to approximate processing as currently being formulated in the computer science community. We then present four examples of signal processing algorithms/systems that are structured with these goals in mind. These examples may be viewed as partial inroads toward the ultimate objective of developing, within the context of signal processing design and implementation,...
SystemLevel Power Optimization: Techniques and Tools
 ACM TRANSACTIONS ON DESIGN AUTOMATION OF ELECTRONIC SYSTEMS
, 2000
"... ..."
EnergyScalable Protocols for BatteryOperated MicroSensor Networks
 IEEE Workshop on Signal Processing Systems
"... To maximize battery lifetimes of distributed wireless sensors, network protocols and data fusion algorithms should be designed with low power techniques. Network protocols minimize energy by using localized communication and control and by exploiting computation/communication tradeoffs. In addit ..."
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Cited by 33 (8 self)
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To maximize battery lifetimes of distributed wireless sensors, network protocols and data fusion algorithms should be designed with low power techniques. Network protocols minimize energy by using localized communication and control and by exploiting computation/communication tradeoffs. In addition, data fusion algorithms such as beamforming aggregate data from multiple sources to reduce data redundancy and enhance signaltonoise ratios, thus further reducing the required communications. We have developed a sensor network system that uses a localized clustering protocol and beamforming data fusion to enable energyefficient collaboration. We have implemented two beamforming algorithms, the Maximum Power and the Least Mean Squares (LMS) beamforming algorithms, on the StrongARM (SA1100) processor. Results from our experiments show that the LMS algorithm requires less than onefifth the energy required by the Maximum Power beamforming algorithm with onlya3dBloss in performa...
A LowPower DCT Core Using Adaptive Bitwidth and Arithmetic Activity . . .
 IEEE JOURNAL OF SOLIDSTATE CIRC.
, 2000
"... This work describes the implementation of a discrete cosine transform (DCT) core compression system targetted to lowpower video (MPEG2 MP@ML) and stillimage (JPEG) applications. It exhibits two innovative techniques for arithmetic operation reduction in the DCT computation context along with stand ..."
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Cited by 27 (3 self)
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This work describes the implementation of a discrete cosine transform (DCT) core compression system targetted to lowpower video (MPEG2 MP@ML) and stillimage (JPEG) applications. It exhibits two innovative techniques for arithmetic operation reduction in the DCT computation context along with standard voltage scaling techniques such as pipelining and parallelism. The first method dynamically minimizes the bitwidth of arithmetic operations in the presence of data spatial correlation. The second method trades off power dissipation and image compression quality (arithmetic precision.) The chip dissipates 4.38 mW at 14 MHz and 1.56 V.
Algorithmic Transforms for Efficient Energy Scalable Computation
, 2000
"... We introduce the notion of energy scalable computation on general purpose processors. The principle idea is to maximize computational quality for a given energy constraint. The desirable energyquality behavior of algorithms is discussed. Subsequently the energyquality scalability of three distinct ..."
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Cited by 26 (5 self)
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We introduce the notion of energy scalable computation on general purpose processors. The principle idea is to maximize computational quality for a given energy constraint. The desirable energyquality behavior of algorithms is discussed. Subsequently the energyquality scalability of three distinct categories of commonly used signal processing algorithms (viz. filtering, frequency domain transforms and classification) are analyzed on the StrongARM SA1100 processor and transformations are described which obtain significant improvements in the energyquality scalability of the algorithm. I. INTRODUCTION In embedded systems, energy is a precious resource and must be used efficiently. Therefore, it is highly desirable that we structure our algorithms and systems in such a fashion that computational accuracy can be traded off with energy requirement. At the heart of such transformations lies the concept of incremental refinement [1]. Consider the scenario where an individual is using his...
A Micropower Programmable DSP Using Approximate Signal Processing Based on Distributed Arithmetic
 IEEE Journal of SolidState Circuits
, 2004
"... A recent trend in lowpower 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 distribute ..."
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Cited by 13 (4 self)
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A recent trend in lowpower 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 lowpower precisionondemand computation. We present an ultralowpower DSP which uses variable precision arithmetic, lowvoltage circuits, and conditional clocks to implement a biomedical detection and classification algorithm using only 560 nW. Low energy consumption enables selfpowered 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 W of power to the DSP load.
Energy Scalable System Design
, 2002
"... We introduce the notion of energyscalable systemdesign. The principal idea is to maximize computational quality for a given energy constraint at all levels of the system hierarchy. The desirable energyquality (EQ) characteristics of systems are discussed. EQ behavior of algorithms is considere ..."
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Cited by 12 (1 self)
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We introduce the notion of energyscalable systemdesign. The principal idea is to maximize computational quality for a given energy constraint at all levels of the system hierarchy. The desirable energyquality (EQ) characteristics of systems are discussed. EQ behavior of algorithms is considered and transforms that significantly improve scalability are analyzed using three distinct categories of commonly used signalprocessing algorithms on the StrongARM SA1100 processor as examples (viz., filtering, frequency domain transforms and classification). Scalability hooks in hardware are analyzed using similar examples on the Pentium III processor and a scalable programming methodology is proposed. Design techniques for true energy scalable hardware are also demonstrated using filtering as an example.
NetworkDriven Motion Estimation for Wireless Video Terminals
 IEEE Transactions on Circuits and Systems for Video Technology
, 1997
"... Motion estimation has been shown to help significantly in the compression of video sequences. However, since most motion estimation algorithms require a large amount of computation, it is undesirable to use them in power constrained applications, such as batteryoperated wireless video encoders. Thi ..."
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Cited by 11 (3 self)
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Motion estimation has been shown to help significantly in the compression of video sequences. However, since most motion estimation algorithms require a large amount of computation, it is undesirable to use them in power constrained applications, such as batteryoperated wireless video encoders. This paper describes a new compression algorithm, termed networkdriven motion estimation (NDME), which reduces the power dissipation of wireless video devices in a networked environment by exploiting the predictability of object motion. Since the location of an object in the current frame can often be predicted accurately from its location in previous frames, it is possible to optimally partition the motion estimation computation between the portable devices and high powered compute servers on the wired network. In networkdriven motion estimation, a remote highpowered resource at the basestation (or on the wired network), predicts the motion vectors of the current frame from the motion vectors of the previous frames. The basestation sends these predicted motion vectors to a portable video encoder, where motion compensation proceeds as usual. Networkdriven motion estimation adaptively adjusts the coding algorithm based on the amount of motion in the sequence, using motion prediction to code portions of the video sequence which contain a large amount of motion and conditional replenishment to code portions of the sequence which contain little scene motion. This algorithm achieves a reduction in the number of operations performed at the encoder for motion estimation by over two orders of magnitude while introducing minimal degradation to the decoded video compared with full search encoderbased motion estimation.
Energy Efficient Filtering Using Adaptive Precision and Variable Voltage
, 1999
"... A Finite Impulse Response (FIR) filter architecture based on a Distributed Arithmetic (DA) approach with two supply voltages and variable bit precision operation is presented. The filter is able to adapt itself to the minimum bit precision required by the incoming data and also operate at a lower vo ..."
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Cited by 11 (4 self)
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A Finite Impulse Response (FIR) filter architecture based on a Distributed Arithmetic (DA) approach with two supply voltages and variable bit precision operation is presented. The filter is able to adapt itself to the minimum bit precision required by the incoming data and also operate at a lower voltage so that it still meets a fixed throughput constraint. As opposed to the worst case fixed precision design, our precisionondemand implementation has an energy requirement that varies linearly with the average bit precision required by the input signal. We also demonstrate that 50% to 60% energy savings can easily be obtained in the case of speech data.
Dynamic Algorithm Transformations (DAT)  A Systematic Approach to LowPower Reconfigurable Signal Processing
 IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS
, 1999
"... In this paper, dynamic algorithm transformations (DAT's) for designing lowpower reconfigurable signalprocessing systems are presented. These transformations minimize energy dissipation while maintaining a specified level of mean squared error or signaltonoise ratio. This is achieved by modeling ..."
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Cited by 10 (2 self)
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In this paper, dynamic algorithm transformations (DAT's) for designing lowpower reconfigurable signalprocessing systems are presented. These transformations minimize energy dissipation while maintaining a specified level of mean squared error or signaltonoise ratio. This is achieved by modeling the nonstationarities in the input as temporal/spatial transitions between states in the input statespace. The reconfigurable hardware fabric is characterized by its configuration statespace. The configurable parameters are taken to be the filter taps, coefficient and data precisions, and supply voltage Vdd . An energyoptimal reconfiguration strategy is derived as a mapping from the input to the configuration statespace. In this strategy, taps are powered down starting with the tap with the smallest value of [w 2 k =Em(wk )] (where wk and Em(wk ) are, respectively, the coefficient and energy dissipation of the kth tap). Optimal values for precisions and supply voltage Vdd are subsequently computed from the roundoff error and critical path delay requirements, respectively. The DATbased adaptive filter is employed as a nearend crosstalk (NEXT) canceller in a 155.52Mb/s asynchronous transfer modelocal area network transceiver over category3 wiring. Simulation results indicate that the energy savings range from 02% to 87% as the cable length varies from 110 to 40 m, respectively, with an average savings of 69%. An average savings of 62% is achieved for the case where the supply voltage Vdd is kept fixed.