In this talk, I will talk about some hardware/software work my group has done in the area of stochastic computing-based machine learning acceleration. Stochastic computing or SC is an approximate, stream-based computing paradigm enabling extremely area-efficient implementations of basic arithmetic operations such as multiplication and addition. I will talk about the suitability of the SC to the machine learning/event processing workloads, how to deal with its inherent approximate nature and briefly discuss few chip prototypes that leverage both logic and in-memory implementations of SC-based accelerators for dense as well as a sparse compute.
Speaker(s): Puneet Gupta,
Virtual: https://events.vtools.ieee.org/m/561732
