Approximate Computing for Low-Power Many-Core Processors

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Developing more efficient computing systems is a critical goal of the high-tech industry. To this end, designers have suggested hardware (i.e., energy efficient components and systems) and software methods (compiler solutions for generating energy efficient code). Developing efficient computing systems not only has resulted in environmental benefits, but has also helped reducing production cost and complexity and achieving longer battery lives. Recent studies however, have shown that the same level of efficiency is not sustainable and that further achievements in developing low-power solutions face difficulties imposed not only by technology limitations but also application requirements.Traditionally, applications running on computing systems have required intact accuracy. In recent years many studies have analyzed computing at the application level and have shown that many applications can tolerate an acceptable level of inaccuracy and approximation in their computations.Approximate computing builds on this observation and is a promising solution to maintaining energy efficiency in the future. Conventional low-power design explored a two dimensional space where trade-offs between power and performance were explored with the goal of maximising energy savings while maintaining performance. With the inclusion of approximation, this design space has been extended to a three dimensional one where accuracy can also be traded for higher efficiency. This extension motivates us to pursue new opportunities and further optimizations. This research will deliver new ways to explore and build approximate-aware systems relying on a less aggressive approach to computing but still capable of delivering acceptable results.We propose a deep analysis of General Purpose Graphic Processing Units (GPGPUs) anddeveloping both hardware and software solutions to reduce energy consumption and improve efficiency while maintaining accuracy within acceptable limits. While our hardware solutions will focus on designing new and efficient Graphic Processing Units, our software solutions will use a compiler based approach to identify opportunities to achieve low-complexity computing. Our past experience with hardware and software optimizations, application behaviour analysis and our familiarity with the tools available provide us with the required skills to develop low-power approximate-aware systems.Our proposed research makes important contributions to the Canadian society. First, the proposed program aims at developing "greener" computing infrastructures where power hungry GPGPUs are replaced with low-power alternatives. Second, the resulting knowledge belongs to an area critical to Canada's future leadership in advanced technologies. Many Canadian companies can benefit from the findings of the proposed research and the resulting HQP training.