Results 1  10
of
30
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 ..."
Abstract

Cited by 363 (2 self)
 Add to MetaCart
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 ..."
Abstract

Cited by 33 (8 self)
 Add to MetaCart
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 ..."
Abstract

Cited by 30 (3 self)
 Add to MetaCart
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 ..."
Abstract

Cited by 27 (5 self)
 Add to MetaCart
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
 JSSC
, 2004
"... ..."
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 ..."
Abstract

Cited by 13 (1 self)
 Add to MetaCart
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
"... ..."
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 ..."
Abstract

Cited by 11 (4 self)
 Add to MetaCart
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 mode ..."
Abstract

Cited by 10 (2 self)
 Add to MetaCart
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.