Learned Approximate Computing: Algorithm Hardware Co-Optimization

Approximate hardware trades acceptable error for improved performance and previous literature focuses on optimizing this tradeoff in the hardware. We show in this article that the application and the hardware can be co-optimized to achieve the best-quality-performance tradeoff. We propose LAC: learned approximate computing to optimize the algorithm and approximate hardware at the same time to maximize quality of output. Our approach allows automatic selection of approximate computing hardware while achieving similar quality as dedicated training for a single hardware configuration. Our improved training algorithm allows simultaneous hardware selection and application optimization without additional runtime overhead. Multihardware setup chooses a separate approximate hardware for each part of an application which allows for more hardware configurations and further improves quality.