Approximate and Stochastic Computing Systems

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Current computer systems still consume a significant amount of power, regardless of the size of the system: given it a smart phone, a personal computer or a computer server. The reason is two-fold: 1) the tiny sizes of basic electronic devices (at a dimension of the nanometer scale) make them susceptible to variation and temporary errors due to manufacturing and environmental factors, so larger-than-necessary voltages and currents are required to ensure the operational reliability; and 2) computing applications have increasingly become complex, such as multimedia, that involve a significant number of arithmetic operations. As a result, energy efficiency has become a paramount concern for current computer systems. The conflict between reliability and energy efficiency seems to be inevitable and presents significant design challenges. On the other hand, however, many computing tasks show a common characteristic of being imprecision-tolerant or error-resilient, particularly during the intermediate computing process. It is an especially important feature in many emerging applications such as image/video processing, pattern recognition and machine learning. The objective of this research program is to develop a new class of computing systems that employs approximate computing (AC) and stochastic computing (SC) techniques for energy-efficient and high-performance processing. AC leverages the error resilience in many applications and employs deliberate and deterministic designs to deliver imprecise but good-enough results, whereas SC uses simplistic hardware with random binary bit streams for producing meaningful statistics in the computed result. Considerable effort has been devoted to the development of basic circuit elements for AC and to the design and implementation of basic building blocks for SC. A challenge, however, is to integrate various circuit components into an AC and/or SC system for low-power and high-performance operation. To address this challenge, the effective integration of various AC and SC circuit components into a larger system will be the primary focus of this research. The design will be aimed at conventional digital signal processing in embedded and mobile systems, as well as emerging brain-inspired computing systems that explore the organization and functions of neurons and connecting synapses at different levels of hierarchy and abstraction. Two essential classes of applications will be considered: 1) multimedia, including image, audio and video processing; and 2) image and voice recognition using effective machine-learning models, including neural networks. Addressing a fundamentally challenging issue, i.e., energy efficiency in computer systems, this research program will produce useful results for the electronics and information industry. It will also provide valuable training opportunities for highly qualified personnel with skills demanded by and crucial to the long-term growth of the Canadian economy.