A High Energy Efficient Reconfigurable Hybrid Neural Network Processor for Deep Learning Applications
Shouyi Yin, Peng Ouyang, Shibin Tang, Fengbin Tu, Xiudong Li, Shixuan Zheng, Tianyi Lu, Jiangyuan Gu, Leibo Liu, Shaojun Wei · IEEE Journal of Solid-State Circuits · 2017
Hybrid neural networks (hybrid-NNs) have been widely used and brought new challenges to NN processors. Thinker is an energy efficient reconfigurable hybrid-NN processor fabricated in 65-nm technology. To achieve high energy efficiency, three optimization techniques are proposed. First, each processing element (PE) supports bit-width adaptive computing to meet various bit-widths of neural layers, which raises computing throughput by 91% and improves energy efficiency by 1.93× on average. Second, PE array supports on-demand array partitioning and reconfiguration for processing different NNs in parallel, which results in 13.7% improvement of PE utilization and improves energy efficiency by 1.11×. Third, a fused data pattern-based multi-bank memory system is designed to exploit data reuse and guarantee parallel data access, which improves computing throughput and energy efficiency by 1.11× and 1.17×, respectively. Measurement results show that this processor achieves 5.09-TOPS/W energy efficiency at most.