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 transistor-level 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
$\times$
3 pixel size.