Energy-Efficient Approximate Computation in Topaz

Abstract

We present Topaz, a new task-based language for computations that execute on approximate computing platforms that may occasionally produce arbitrarily inaccurate results. The Topaz implementation maps approximate tasks onto the approximate machine and integrates the approximate results into the main computation, deploying a novel outlier detection and reliable reexecution mechanism to prevent unacceptably inaccurate results from corrupting the overall computation. Topaz therefore provides the developers of approximate hardware with substantial freedom in producing designs with little or no precision or accuracy guarantees. Experimental results from our set of benchmark applications demonstrate the effectiveness of Topaz and the Topaz implementation in enabling developers to productively exploit emerging approximate hardware platforms.