Bayat, Farnood Merrikh
(ECE Dept., Univ. of California Santa Barbara, Santa Barbara, CA, USA)
,
Xinjie Guo
(ECE Dept., Univ. of California Santa Barbara, Santa Barbara, CA, USA)
,
Strukov, Dmitri
Analog integrated circuits may increase the neuromorphic network performance dramatically, leaving far behind their digital and biological counterparts, while approaching the energy efficiency of the brain. The key component of the most advanced analog circuit implementations is a nanodevice with ad...
Analog integrated circuits may increase the neuromorphic network performance dramatically, leaving far behind their digital and biological counterparts, while approaching the energy efficiency of the brain. The key component of the most advanced analog circuit implementations is a nanodevice with adjustable conductance - essentially an analog nonvolatile memory cell, which could mimic synaptic transmission function by multiplying signal from the input neuron (e.g. encoded as voltage applied to the memory device) by its analog weight (device conductance) and passing the product (the resulting current) to the output neuron. Such functionality enables very dense, fast, and low power implementation of dot-product computation, the most common operation in many artificial neural networks. The most promising analog memory devices, however, have nonlinear, typically exponential, I-V characteristics, which result in nonlinear synaptic transmission, thus limiting their application in analog dot-product circuits. Here we investigate multilayer perceptron with exponential transmission function synapses which maps naturally to the most advanced analog neuromorphic circuits. Our simulation results show that the proposed exponential-weight multilayer perceptron with 300 hidden neurons achieves classification performance comparable to the similar-size linear-weight network when benchmarked on MNIST dataset. Moreover, we verify the proposed idea experimentally by implementing small-scale single-layer exponential-weight perceptron classifier with an NOR-flash memory integrated circuit.
Analog integrated circuits may increase the neuromorphic network performance dramatically, leaving far behind their digital and biological counterparts, while approaching the energy efficiency of the brain. The key component of the most advanced analog circuit implementations is a nanodevice with adjustable conductance - essentially an analog nonvolatile memory cell, which could mimic synaptic transmission function by multiplying signal from the input neuron (e.g. encoded as voltage applied to the memory device) by its analog weight (device conductance) and passing the product (the resulting current) to the output neuron. Such functionality enables very dense, fast, and low power implementation of dot-product computation, the most common operation in many artificial neural networks. The most promising analog memory devices, however, have nonlinear, typically exponential, I-V characteristics, which result in nonlinear synaptic transmission, thus limiting their application in analog dot-product circuits. Here we investigate multilayer perceptron with exponential transmission function synapses which maps naturally to the most advanced analog neuromorphic circuits. Our simulation results show that the proposed exponential-weight multilayer perceptron with 300 hidden neurons achieves classification performance comparable to the similar-size linear-weight network when benchmarked on MNIST dataset. Moreover, we verify the proposed idea experimentally by implementing small-scale single-layer exponential-weight perceptron classifier with an NOR-flash memory integrated circuit.
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