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Full CMOS Implementation of Bidirectional Associative Memory Neural Network
Full CMOS Implementation of Bidirectional Associative Memory Neural Network
Memristor is a computational and area-efficient substitute for resistive synapse in neural networks as it provides tunable and non-volatile storage of synaptic weights. We have realised full CMOS circuit of a 6 × 6 Bidirectional Associative Memory (BAM) neural network with CMOS memristor synapse. To show the BAM neural network’s ability to recall its pattern, we have taken the training set of three Tetris pattern pairs, and corresponding synapse weights are calculated using MATLAB. We have integrated the proposed tunable CMOS memristor emulator in the crossbar for storing the synaptic weights. The CMOS circuit implementation of the BAM neural network is validated with the simulation results in 0.18µm CMOS technology.
Circuit Implementation of On-chip Trainable Spiking Neural Network using CMOS Based Memristive STDP Synapses and LIF Neurons
Circuit Implementation of On-chip Trainable Spiking Neural Network using CMOS Based Memristive STDP Synapses and LIF Neurons
We demonstrated a full circuit-level implementation of the SNN system featuring on-chip training and classification using memristive STDP synapse in standard CMOS technology. It does not involve FPGAs, CPUs, or GPUs to train the neural network. The approach used to modify the weights does not require any additional combinational or digital circuits attached to the memristive synapse resulting in less consumption of area, energy and time. We demonstrated the complete circuit-level design, implementation and simulation of SNN with on-chip training and pattern classification using 180 nm CMOS technology.
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