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Neuromorphic computing using biologically inspired Spiking Neural Networks(SNNs) is a promising solution to meet Energy-Throughput (ET) efficiency neededfor edge computing devices. Neuromorphic hardware architectures that emulateSNNs in analog/mixed-signal domains have been proposed to achieveorder-of-magnitude higher energy efficiency than all-digital architectures,however at the expense of limited scalability, susceptibility to noise, complexverification, and poor flexibility. On the other hand, state-of-the-art digitalneuromorphic architectures focus either on achieving high energy efficiency(Joules/synaptic operation (SOP)) or throughput efficiency (SOPs/second/area),resulting in poor ET efficiency. In this work, we present THOR, an all-digitalneuromorphic processor with a novel memory hierarchy and neuron updatearchitecture that addresses both energy consumption and throughput bottlenecks.We implemented THOR in 28nm FDSOI CMOS technology and our post-layout resultsdemonstrate an ET efficiency of 7.29G $\text{TSOP}^2/\text{mm}^2\text{Js}$ at0.9V, 400 MHz, which represents a 3X improvement over state-of-the-art digitalneuromorphic processors.

THOR -- A Neuromorphic Processor with 7.29G TSOP$^2$/mm$^2$Js Energy-Throughput Efficiency