Design Principles of Large-Scale Neuromorphic Systems Centered on High Bandwidth Memory

Abstract

In order for neuromorphic computing to attain full throughput capacity, its hardware design must mitigate any inefficiencies that result from limited bandwidth to neural and synaptic information. In large-scale neuromorphic systems, synaptic memory access is typically the defining bottleneck, demanding that system design closely analyze the interdependence between the functional blocks to keep the memory as active as possible. In this paper, we formulate principles in memory organization of digital spiking neural networks, with a focus on systems with High Bandwidth Memory (HBM) as their bulk memory element. We present some of the fundamental steps and considerations required when designing a highly efficient HBM-centric system, and describe parallelization and pipelining solutions which serve as a foundational architecture for streamlined operation in any multi-port memory system. In our experiments using the Xilinx VU37P FPGA, we demonstrate random, short burst-length memory read bandwidths in excess of 400 GBps (95% relative to sequential-access peak bandwidth), supporting dynamically reconfigurable sparse synaptic connectivity. Therefore, the combination of our proposed network model with practical results suggest a promising path towards implementing highly parallel large-scale neuromorphic systems centered on HBM.