SDM-SNN: Sparse Distributed Memory Using Constant-Weight Fibonacci Code for Spiking Neural Network

The long-term memory (LTM) is generally considered as the unlimited and permanent storage of information in the human brain. This concept has spurred numerous researches focusing on associative memory models, such as Sparse Distributed Memory (SDM) in the past, as well as the increasing interest in brain-inspired Spiking Neural Networks (SNN). In this paper, we present an SDM-like SNN, which combines elements of both SDM and SNN for unsupervised training and inference processing. Despite the hardware-friendly nature of event-driven processing, SNN still faces criticism due to its large memory footprint and repetitive neuron operations. Therefore, the objective of this study is to demonstrate the complementary nature of SDM and SNN, with the aim of significantly reducing energy consumption while maintaining an acceptable level of accuracy loss on neuromorphic computing systems. As an integral part of the SDM-like design, we have developed and thoroughly tested an M-of-N code generation method. The experimental results on the MNIST dataset demonstrated that the proposed constant-weight Fibonacci code SDM-SNN achieves 87.4% accuracy with a simplified 2-bit unsupervised learning rule. In addition, with only 400 neurons, the proposed SDM-SNN hardware architecture reduces numerous energy consumption to 4.5-nJ/classification and 7.5 pJ/SOP with just a little performance degradation
$(\sim 3.7\%)$
compared to the SOTA full-connected SNN network.