Committee: 

Dr. Raychowdhury, Advisor  

Dr. Lim, Chair

Dr. Romberg

Abstract:

The objective of the proposed research is to enable computation close to the sensor. Internet of Things (IoT) devices are collecting a large amount of data for video processing, monitoring health etc. Transmitting the data from the sensor to the cloud requires a large aggregate bandwidth. The objective of the proposed research is to leverage advances in machine learning to perform in-sensor computation, thus reducing the transmission bandwidth, preserving data privacy and enabling low-power operation. The proposed research demonstrates a system design and IC designs to achieve energy efficiency. As a system prototype, we demonstrate a light-powered always ON gesture recognition system. As circuit innovations, we demonstrate voltage and time based matrix multiplying ADCs (MMADCs), compressive sensing ADCs (CS-ADCs) along with measurement results. The proposed time based MMADC is digitally synthesizable, can operate at a supply as low supply as 0.4V and demonstrates higher energy efficiency compared to the state of the art designs.