Multiplexer: Difference between revisions

It is not an exact addition but a scaled-addition, which is acceptable in most SC studies. It is widely used for average adding, average pooling and median filtering in the SC circuits. More advanced use case for Multiplexer includes Bernstein polynomial function generator <ref>{{Cite journal |last=Najafi |first=M. Hassan |last2=Li |first2=Peng |last3=Lilja |first3=David J. |last4=Qian |first4=Weikang |last5=Bazargan |first5=Kia |last6=Riedel |first6=Marc |date=2017-06-29 |title=A Reconfigurable Architecture with Sequential Logic-Based Stochastic Computing |url=https://dl.acm.org/doi/10.1145/3060537 |journal=ACM Journal on Emerging Technologies in Computing Systems |volume=13 |issue=4 |pages=57:1–57:28 |doi=10.1145/3060537 |issn=1550-4832}}</ref>, which could generate arbitrary math function in the SC domain. New study also found that Multiplexer combination can perform large-scale [[Multiply–accumulate operation|multiply-accumulate]] operation<ref>{{Cite journal |last=Lee |first=Yang Yang |last2=Halim |first2=Zaini Abdul |last3=Wahab |first3=Mohd Nadhir Ab |last4=Almohamad |first4=Tarik Adnan |date=2024-0103-04 |title=Stochastic Computing Convolutional Neural Network Architecture Reinvented for Highly Efficient Artificial Intelligence Workload on Field-Programmable Gate Array |url=https://spj.science.org/doi/10.34133/research.0307 |journal=Research |language=en |volume=7 |doi=10.34133/research.0307 |issn=2639-5274 |pmc=PMC10911856 |pmid=38439995}}</ref>, and it is proven feasable to accelerate [[convolutional neural network]] on [[field-programmable gate array]].