Attention-Based Secure Feature Extraction in Near Sensor Processing: Work-in-Progress

Abstract

This paper presents a secure hardware architecture of an image sensor to accelerate feature extraction using region-level parallelism. For each logical region, the design includes a region processing unit (RPU) with an attention module (AM). The AM activates the processing in the RPU if there are no spatiotemporal redundancies. It reduces power consumption and data volume by utilizing the concepts of predictive coding. Also, every RPU has a crypto-core driven by the AM to withstand against adversaries. Simulation results show we can save 89.70% power with a significant speedup.