A 1.06-to-5.09 TOPS/W reconfigurable hybrid-neural-network processor for deep learning applications

Abstract

An energy-efficient hybrid neural network (NN) processor is implemented in a 65nm technology. It has two 16×16 reconfigurable heterogeneous processing elements (PEs)arrays. To accelerate a hybrid-NN, the PE array is designed to support on demand partitioning and reconfiguration for parallel processing different NNs. To improve energy efficiency, each PE supports bit-width adaptive computing to meet variant bit-width of different neural layers. Measurement results show that this processor achieves a peak 409.6GOPS running at 200MHz and at most 5.09TOPS/W energy efficiency. This processor outperforms the state-of-the-art up to 5.2X in energy efficiency.