Design of Stochastic Computing Architectures using Integrated Optics

Approximate computing (AC) is an emerging computing approach that allows to trade off design energy efficiency with computing accuracy. It targets error resilient applications, such as image processing, where energy consumption is of major concern. Stochastic computing (SC) is an approximate computing paradigm that leads to energy efficient and reduced hardware complexity designs. In this approach, data is represented as probabilities in bit streams format. The main drawback of this computing paradigm is the intrinsic serial processing of bit streams, which negatively impacts the processing time. Nanophotonics technology is characterized by high bandwidth and high signals propagation speed, which has the potential to support the electrical domain in computations to speed up the processing rate. The major issues in optical computing (OC) remain the large size of silicon photonics devices, which impact the design scalability. In this thesis, we propose, for the first time, an optical stochastic computing (OSC) approach, where we aim to design SC architectures using integrated optics. For this purpose, we propose a methodology that has libraries for optical processing and interfaces, e.g., bit stream generator. We design all-optical gates for the computation and develop transmission models for the architectures. The methodology allows for design space exploration of technological and system-level parameters to optimize design performance, i.e., energy efficiency, computing accuracy, and latency, for the targeted application. This exploration leads to multiple design options that satisfy different design requirements for the selected application. The optical processing libraries include designing a polynomial architecture that can execute any arbitrary single input function. We explore the design parameters by implementing a Gamma correction application for image processing. Results show a 4.5x increase in the errors, which leads to 47x energy saving and 16x faster processing speed. We propose a reconfigurable polynomial architecture to adapt design order at run-time. The design allows the execution of high order polynomial functions for better accuracy or multiple low order functions to increase throughput and energy efficiency. Finally, we propose the design of combinational filters. The purpose is to investigate the design of cascaded gates architectures using photonic crystal (PhC) nanocavities. We use this device to design a Sobel edge detection filter for image processing. The resulting architecture shows 0.85nJ/pixel energy consumption and 51.2ns/pixel processing time. The optical interface libraries include designing different architectures of stochastic number generators (SNG) that are either electrical-optical or all-optical to generate the bit streams. We compare these SNGs in terms of computing accuracy and energy efficiency. The results show that all implementations can lead to the same level of computing accuracy. Moreover, using an all-optical SNG to design a fully optical 8-bit adder results in 98% reduction in hardware complexity and 70% energy saving compared to a conventional optical design