Grayscale Image Recognition Using Spike-Rate-Based Online Learning and Threshold Adjustment of Neurons in a Thin-Film Transistor-Type NOR Flash Memory Array.

Abstract

As a synaptic device, TFT-type NOR flash memory cell shows reasonable weight levels (50 levels for long-term potentiation (LTP) and 150 levels for long-term depression (LTD)) and large max/min ratio (═50) for synapse weight. Based on the measurement results of the synapse cell, supervised learning process is simulated using software MATLAB. A new pulse scheme is designed for mimicking spike-rate-dependent plasticity (SRDP) algorithm. Through learning and inferencing phase, our (784 × 100) network achieved 74.08% accuracy on the MNIST benchmark. A new method for adapting the threshold voltage of output neurons for firing is also proposed. This additional adjustment helps to eliminate the exclusive or dormant output neurons by setting the threshold voltage to an appropriate value proportional to the average weight of synapses connected to each neuron. As a result, accuracy increases to 82.54% in the (784 × 100) network and to 84.14% in the (784 × 200) network. Moreover, threshold adjustment helped the network to classify completely overlapped patterns in succession.