TY - JOUR
T1 - Computation on stochastic bit streams digital image processing case studies
AU - Li, Peng
AU - Lilja, David J
AU - Qian, Weikang
AU - Bazargan, Kia
AU - Riedel, Marc
PY - 2014/3
Y1 - 2014/3
N2 - Maintaining the reliability of integrated circuits as transistor sizes continue to shrink to nanoscale dimensions is a significant looming challenge for the industry. Computation on stochastic bit streams, which could replace conventional deterministic computation based on a binary radix, allows similar computation to be performed more reliably and often with less hardware area. Prior work discussed a variety of specific stochastic computational elements (SCEs) for applications such as artificial neural networks and control systems. Recently, very promising new SCEs have been developed based on finite-state machines (FSMs). In this paper, we introduce new SCEs based on FSMs for the task of digital image processing. We present five digital image processing algorithms as case studies of practical applications of the technique. We compare the error tolerance, hardware area, and latency of stochastic implementations to those of conventional deterministic implementations using binary radix encoding. We also provide a rigorous analysis of a particular function, namely the stochastic linear gain function, which had only been validated experimentally in prior work.
AB - Maintaining the reliability of integrated circuits as transistor sizes continue to shrink to nanoscale dimensions is a significant looming challenge for the industry. Computation on stochastic bit streams, which could replace conventional deterministic computation based on a binary radix, allows similar computation to be performed more reliably and often with less hardware area. Prior work discussed a variety of specific stochastic computational elements (SCEs) for applications such as artificial neural networks and control systems. Recently, very promising new SCEs have been developed based on finite-state machines (FSMs). In this paper, we introduce new SCEs based on FSMs for the task of digital image processing. We present five digital image processing algorithms as case studies of practical applications of the technique. We compare the error tolerance, hardware area, and latency of stochastic implementations to those of conventional deterministic implementations using binary radix encoding. We also provide a rigorous analysis of a particular function, namely the stochastic linear gain function, which had only been validated experimentally in prior work.
KW - Digital image processing
KW - fault tolerance
KW - finite state machine (FSM)
KW - stochastic computing
UR - http://www.scopus.com/inward/record.url?scp=84895925997&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84895925997&partnerID=8YFLogxK
U2 - 10.1109/TVLSI.2013.2247429
DO - 10.1109/TVLSI.2013.2247429
M3 - Article
AN - SCOPUS:84895925997
SN - 1063-8210
VL - 22
SP - 449
EP - 462
JO - IEEE Transactions on Very Large Scale Integration (VLSI) Systems
JF - IEEE Transactions on Very Large Scale Integration (VLSI) Systems
IS - 3
M1 - 6502263
ER -