We present the design of stochastic computing systems based on sequential logic to implement arbitrary polynomial functions. Stochastic computing is an emerging alternative computing paradigm that performs arithmetic operations on real-valued data represented as random bitstreams using digital logic gates. Stochastic computing systems are capable of realizing complex mathematical operations using a small number of hardware resources by expressing the computation in terms of probabilities. Moreover, the stochastic representation of data using random bitstreams is extremely robust against bit errors. We present a systematic approach to implement arbitrary polynomial functions in stochastic computing using sequential logic, and compare our approach against prior conventional and stochastic implementations.
|Original language||English (US)|
|Title of host publication||GLSVLSI 2016 - Proceedings of the 2016 ACM Great Lakes Symposium on VLSI|
|Publisher||Association for Computing Machinery|
|Number of pages||6|
|State||Published - May 18 2016|
|Event||26th ACM Great Lakes Symposium on VLSI, GLSVLSI 2016 - Boston, United States|
Duration: May 18 2016 → May 20 2016
|Name||Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI|
|Other||26th ACM Great Lakes Symposium on VLSI, GLSVLSI 2016|
|Period||5/18/16 → 5/20/16|
Bibliographical noteFunding Information:
This material is based on work supported by the National Science Foundation under grant CCF-1408123.