2022/02/08 by Sangyeob Kim, Kim, Sangyeob, Sangjin Kim +7
Computer Science · Engineering · Neuroscience · #Advanced Memory and Neural Computing #FOS: Computer and information sciences #Hardware Architecture (cs.AR) #Neural Networks and Reservoir Computing #Neural dynamics and brain function
paper · pdf · doi:10.48550/arxiv.2202.03601
openalex publication_date 2022/02/08 · openalex created_date 2022/04/03 · openalex updated_date 2026/07/28
This paper proposes a two-step spike encoding scheme, which consists of the source encoding and the process encoding for a high energy-efficient spiking-neural-network (SNN) acceleration. The eigen-train generation and its superposition generate spike trains which show high accuracy with low spike ratio. Sparsity boosting (SB) and spike generation skipping (SGS) reduce the amount of operations for SNN. Time shrinking multi-level encoding (TS-MLE) compresses the number of spikes in a train along time axis, and spike-level clock skipping (SLCS) decreases the processing time. Eigen-train generation achieves 90.3% accuracy, the same accuracy of CNN, under the condition of 4.18% spike ratio for CIFAR-10 classification. SB reduces spike ratio by 0.49x with only 0.1% accuracy loss, and the SGS reduces the spike ratio by 20.9% with 0.5% accuracy loss. TS-MLE and SLCS increases the throughput of SNN by 2.8x while decreasing the hardware resource for spike generator by 75% compared with previous generators.