SpiMAM: CMOS Implementation of Bio-Inspired Spiking Multidirectional Associative Memory Featuring In-Situ Learning

Associative memory (AM) robustly retrieves information from given partial data. Compared to artificial neural network (ANN)-based AM, spiking neural network (SNN)-based AM offers greater bio-plausibility, sparsity, and message storage capacity. Recently, an ANN-based multidirectional associative memory neural network (MAMNN) for handling multiple associations was implemented by extending an ANN-based bidirectional associative memory (BAM) neural network. In comparison, this study implements SpiMAM, a more bio-plausible MAMNN based on SNN with a winner-take-all mechanism. The circuit design of spiking MAMNN (SpiMAM) employing in-situ synaptic training was proposed for the first time. Instead of a memristor device or memristor model, a CMOS circuit of a memristive synapse featuring spike-timing-dependent-plasticity (STDP) is used to incorporate the CMOS integrated circuit challenges. The synaptic weights in the crossbar for storage and association of patterns were trained on-chip without requiring additional computing platforms and digital circuitry attached to the synapse. The entire circuit of the spiking MAMNN was implemented at the transistorlevel in 180 nm standard CMOS technology to demonstrate pattern recognition applications. The robustness of the proposed circuit of SpiMAM was evaluated through post-layout simulations for PVT, mismatch variation, pixel flip, hard faults, memristive drifts, and Gaussian noise. Compared to the previous work, this work uses 86 % fewer synapses and 70 % fewer neurons for the pattern recognition of nine binary images of 5 x 3 pixel size.