Mixed-signal stochastic computation demonstrated in an image sensor with integrated 2D edge detection and noise filtering

In this work we describe mixed-signal stochastic computing (MSSC) and demonstrate how it can be used to efficiently integrate computation into a signal path before data conversion. MSSC performs computation directly on the analog values output by sensors, which enables MSSC to combine the area efficiency of traditional stochastic computing with the information density and performance of analog computation. To demonstrate this technology we integrated MSSC between pixel bitlines and the ADC in an image sensor, enabling in situ latency-free edge detection and noise filtering. The MSSC implementation is found to be 2.75× lower power than a traditional digital synthesis implementation while simultaneously requiring 5× lower area.