Stochastic circuits for real-time image-processing applications

Abstract

Real-time image-processing applications impose severe design constraints in terms of area and power. Examples of interest include retinal implants for vision restoration and on-the-fly feature extraction. This work addresses the design of image-processing circuits using stochastic computing techniques. We show how stochastic circuits can be integrated at the pixel level with image sensors, thus supporting efficient real-time (pre)processing of images. We present the design of several representative circuits, which demonstrate that stochastic designs can be significantly smaller, faster, more power-efficient, and more noise-tolerant than conventional ones. Furthermore, the stochastic designs naturally produce images with progressive quality improvement.

Cheng Li

Senior Researcher

My work focus on optimizing inference/training of Deep Learning models, particularly on Transformers (LLMs).