An Energy-Efficient Sparse Deep-Neural-Network Learning Accelerator With Fine-Grained Mixed Precision of FP8-FP16

Recently, several hardware have been reported for deep-neural-network (DNN) acceleration, however, they focused on only inference rather than DNN learning that is crucial ingredient for user adaptation at the edge-device as well as transfer learning with domain-specific data. However, DNN learning requires much heavier floating-point (FP) computation and memory access than DNN inference, thus, dedicated DNN learning hardware is essential. In this letter, we present an energy-efficient DNN learning accelerator core supporting CNN and FC learning as well as inference with following three key features: 1) fine-grained mixed precision (FGMP); 2) compressed sparse DNN learning/inference; and 3) input load balancer. As a result, energy efficiency is improved 1.76x compared to sparse FP16 operation without any degradation of learning accuracy. The energy efficiency is 4.9x higher than NVIDIA V100 GPU and its normalized peak performance is 3.47x higher than previous DNN learning processor.