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Energy-efficient multiplier-less discrete convolver through probabilistic domain transformation

Published: 26 February 2014 Publication History

Abstract

Energy efficiency and algorithmic robustness typically are conflicting circuit characteristics, yet with CMOS technology scaling towards 10-nm feature size, both become critical design metrics simultaneously for modern logic circuits. This paper propose a novel computing scheme hinged on probabilistic domain transformation aiming for both low power operation and fault resilience. In such a computing paradigm, algorithm inputs are first encoded through probabilistic means, which translates the input values into a number of random samples. Subsequently, light-weight operations, such as sim- ple additions will be performed onto these random samples in order to generate new random variables. Finally, the resulting random samples will be decoded probabilistically to give the final results.

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D. Wilhelm and J. Bruck, "Stochastic switching circuit synthesis," in Information Theory, 2008. ISIT 2008. IEEE International Symposium on, pp. 1388--1392, july 2008.
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P.-L. Loh, H. Zhou, and J. Bruck, "The robustness of stochastic switching networks," in Information Theory, 2009. ISIT 2009. IEEE International Symposium on, pp. 2066--2070, 28 2009-july 3 2009.
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H. Zhou and J. Bruck, "On the expressibility of stochastic switching circuits," in Information Theory, 2009. ISIT 2009. IEEE International Symposium on, pp. 2061--2065, 28 2009-july 3 2009.
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W. Qian, X. Li, M. Riedel, K. Bazargan, and D. Lilja, "An architecture for fault-tolerant computation with stochastic logic," Computers, IEEE Transactions on, vol. 60, pp. 93--105, jan. 2011.
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W. Feller, AN INTRODUCTION TO PROBABILITY THEORY AND ITS APPLICATIONS, 2ND ED. No. v. 1 in Wiley publication in mathematical statistics, Wiley India Pvt. Limited, 2008.

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    cover image ACM Conferences
    FPGA '14: Proceedings of the 2014 ACM/SIGDA international symposium on Field-programmable gate arrays
    February 2014
    272 pages
    ISBN:9781450326711
    DOI:10.1145/2554688
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 26 February 2014

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    1. probabilistic

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