AIRCHITECT: Automating Hardware Architecture and Mapping Optimization

Design space exploration and optimization is an essential but iterative step in custom accelerator design involving costly search based method to extract maximum performance and energy efficiency. State-of-the-art methods employ data centric approaches to reduce the cost of each iteration but still rely on search algorithms to obtain the optima. This work proposes a learned, constant time optimizer that uses a custom recommendation network called AIRCHITECT, which is capable of learning the architecture design and mapping space with a 94.3% test accuracy, and predicting optimal configurations, which achieve on an average (GeoMean) 99.9% of the best possible performance on a test dataset with 105 GEMM (GEneral Matrix-matrix Multiplication) workloads.