High-throughput Near-Memory Processing on CNNs with 3D HBM-like Memory

Abstract

This article discusses the high-performance near-memory neural network (NN) accelerator architecture utilizing the logic die in three-dimensional (3D) High Bandwidth Memory– (HBM) like memory. As most of the previously reported 3D memory-based near-memory NN accelerator designs used the Hybrid Memory Cube (HMC) memory, we first focus on identifying the key differences between HBM and HMC in terms of near-memory NN accelerator design. One of the major differences between the two 3D memories is that HBM has the centralized through- silicon-via (TSV) channels while HMC has distributed TSV channels for separate vaults. Based on the observation, we introduce the Round-Robin Data Fetching and Groupwise Broadcast schemes to exploit the centralized TSV channels for improvement of the data feeding rate for the processing elements. Using synthesized designs in a 28-nm CMOS technology, performance and energy consumption of the proposed architectures with various dataflow models are evaluated. Experimental results show that the proposed schemes reduce the runtime by 16.4–39.3% on average and the energy consumption by 2.1–5.1% on average compared to conventional data fetching schemes.