Supporting Approximate Computing On Coarse Grained Re Configurable Array Accelerators
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Supporting Approximate Computing on Coarse Grained Re-configurable Array Accelerators
Author | : Jonathan Dickerson |
Publisher | : |
Total Pages | : 56 |
Release | : 2019 |
Genre | : Accelerator-driven systems |
ISBN | : |
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Recent research has shown approximate computing and Course-Grained Reconfigurable Arrays (GGRAs) are promising computing paradigms to reduce energy consumption in a compute intensive environment. CGRAs provide a promising middle ground between energy inefficient yet flexible Freely Programmable Gate Arrays (FPGAs) and energy efficient yet inflexible Application Specific Integrated Circuits (ASICs). With the integration of approximate computing in CGRAs, there is substantial gains in energy efficiency at the cost of arithmetic precision. However, some applications require a certain percent of accuracy in calculation to effectively perform its task. The ability to control the accuracy of approximate computing during run-time is an emerging topic. This paper presents a rudimentary way to have run-time control of approximation on the CGRA by profiling a function, then generating tables to meet the given approximation accuracy. During the profiling stage, the application is run with all types of approximation, which produces a file that contains the errors for all approximation types (zero, first, third) and the exact value. After the profiling stage, the output is parsed and a table is created with the highest order of approximation type possible and the associated error. Using the auto-generated table, the given tolerance is achieved, while maintaining the highest order of approximation type, which yields the best power savings. The simulation records the metrics associated with each approximation type, which it uses to calculate the achieved power savings for each run.
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