Enhancing SNN Training Performance: A Mixed-Signal Triplet Reconfigurable STDP Circuit with Multiplexing Encoding

In spike-timing-dependent plasticity (STDP), synap-tic weights are modified according to the relative time difference between pre and post-synaptic spikes of spiking neural network (SNN). A triplet STDP model was proposed since this model can better take account of a series of spikes and thus more closely mimic the activity in biological neural systems. Circuit that can switch between different STDP rules was also introduced to improve the range of STDP applications. To apply the advantages of triplet STDP to various tasks, a mixed-signal triplet reconfigurable STDP circuit and its hardware prototype are proposed in this paper. The performance analysis of the STDP training algorithm is carried out with a hardware testbench as well as Pytorch-based SNN. This triplet STDP design achieves 3.28% and 3.63% higher accuracy than the pair STDP learning rule through datasets such as MNIST and CIFAR-10. Our design shows one of the best reconfigurability while keeping a relatively low energy per spike operation (SOP) through the performance comparison with the state of the arts.