Stochastic computing, which employs random bit streams for computations, has shown low hardware cost and high fault-tolerance compared to the computations using a conventional binary encoding. Finite state machine (FSM) based stochastic computing elements can compute complex functions, such as the exponentiation and hyperbolic tangent functions, more efficiently than those using combinational logic. However, the FSM, as a sequential logic, cannot be directly implemented in parallel like the combinational logic, so reducing the long latency of the calculation becomes difficult. Applications in the relatively higher frequency domain would require an extremely fast clock rate using FSM. This paper proposes a parallel implementation of the FSM, using an estimator and a dispatcher to directly initialize the FSM to the steady state. Experimental results show that the outputs of four typical functions using the parallel implementation are very close to those of the serial version. The parallel FSM scheme further shows equivalent or better image quality than the serial implementation in two image processing applications Edge Detection and Frame Difference.
|Original language||English (US)|
|Title of host publication||2018 19th International Symposium on Quality Electronic Design, ISQED 2018|
|Publisher||IEEE Computer Society|
|Number of pages||6|
|State||Published - May 9 2018|
|Event||19th International Symposium on Quality Electronic Design, ISQED 2018 - Santa Clara, United States|
Duration: Mar 13 2018 → Mar 14 2018
|Name||Proceedings - International Symposium on Quality Electronic Design, ISQED|
|Other||19th International Symposium on Quality Electronic Design, ISQED 2018|
|Period||3/13/18 → 3/14/18|
Bibliographical noteFunding Information:
VII. ACKNOWLEDGEMENT We thank the anonymous reviewers for their comments towards improving this paper. We are grateful for resources from the University of Minnesota Supercomputing Institute. This work was supported in part by National Science Foundation grant no. CCF-1241987. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF.
© 2018 IEEE.