Stochastic Computing with Simulated Event Camera Data
Document Type
Conference Proceeding
Date of Original Version
8-9-2021
Abstract
This paper presents initial results of the first time event camera data is being paired with a stochastic computing system for image processing applications in a near-sensor neural network system. Stochastic computers have great potential to reduce the size, complexity, and power required to complete common computing tasks. However, stochastic computing re-quires data to be spread across time in pseudorandom bit-strings; producing these bit-streams is costly from a power perspective. The saccades observed by the event camera can be used to generate pseudo-random bit-streams at the camera's output. Therefore, it is natural to pair these two systems together. The system is described and performance results are shown from pairing simulated Dynamic Vision System (DVS) event camera data with a MATLAB simulation of our Field-Programmable Gate Array-based stochastic computing system.
Publication Title, e.g., Journal
Midwest Symposium on Circuits and Systems
Volume
2021-August
Citation/Publisher Attribution
Stangebye, Theo, Matthew Carrano, Scott Koziol, Eugene Chabot, and John DIcecco. "Stochastic Computing with Simulated Event Camera Data." Midwest Symposium on Circuits and Systems 2021-August, (2021). doi: 10.1109/MWSCAS47672.2021.9531819.