The synthesis of robust polynomial arithmetic with stochastic logic
As integrated circuit technology plumbs ever greater depths in the scaling of feature sizes, maintaining the paradigm of deterministic Boolean computation is increasingly challenging. Indeed, mounting concerns over noise and uncertainty in signal values motivate a new approach: the design of stochas...
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| Published in | 2008 45th ACM/IEEE Design Automation Conference pp. 648 - 653 |
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| Main Authors | , |
| Format | Conference Proceeding |
| Language | English |
| Published |
New York, NY, USA
ACM
08.06.2008
IEEE |
| Series | ACM Conferences |
| Subjects | |
| Online Access | Get full text |
| ISBN | 1605581151 9781605581156 |
| ISSN | 0738-100X |
| DOI | 10.1145/1391469.1391636 |
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| Abstract | As integrated circuit technology plumbs ever greater depths in the scaling of feature sizes, maintaining the paradigm of deterministic Boolean computation is increasingly challenging. Indeed, mounting concerns over noise and uncertainty in signal values motivate a new approach: the design of stochastic logic, that is to say, digital circuitry that processes signals probabilistically, and so can cope with errors and uncertainty. In this paper, we present a general methodology for synthesizing stochastic logic for the computation of polynomial arithmetic functions, a category that is important for applications such as digital signal processing. The method is based on converting polynomials into a particular mathematical form --- Bernstein polynomials --- and then implementing the computation with stochastic logic. The resulting logic processes serial or parallel streams that are random at the bit level. In the aggregate, the computation becomes accurate, since the results depend only on the precision of the statistics. Experiments show that our method produces circuits that are highly tolerant of errors in the input stream, while the area-delay product of the circuit is comparable to that of deterministic implementations. |
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| AbstractList | As integrated circuit technology plumbs ever greater depths in the scaling of feature sizes, maintaining the paradigm of deterministic Boolean computation is increasingly challenging. Indeed, mounting concerns over noise and uncertainty in signal values motivate a new approach: the design of stochastic logic, that is to say, digital circuitry that processes signals probabilistically, and so can cope with errors and uncertainty. In this paper, we present a general methodology for synthesizing stochastic logic for the computation of polynomial arithmetic functions, a category that is important for applications such as digital signal processing. The method is based on converting polynomials into a particular mathematical form --- Bernstein polynomials --- and then implementing the computation with stochastic logic. The resulting logic processes serial or parallel streams that are random at the bit level. In the aggregate, the computation becomes accurate, since the results depend only on the precision of the statistics. Experiments show that our method produces circuits that are highly tolerant of errors in the input stream, while the area-delay product of the circuit is comparable to that of deterministic implementations. |
| Author | Riedel, Marc D. Qian, Weikang |
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| Keywords | probabilistic logic stochastic logic polynomial arithmetic |
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| SubjectTerms | Arithmetic Circuits Hardware -- Hardware test Hardware -- Integrated circuits -- Logic circuits -- Combinational circuits Hardware -- Robustness Logic Polynomial Arithmetic Polynomials Probabilistic Logic Robustness Signal processing Signal synthesis Stochastic Logic Stochastic processes Stochastic resonance Uncertainty |
| Title | The synthesis of robust polynomial arithmetic with stochastic logic |
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