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All-Passive Hardware Implementation of Multilayer Perceptron Classifiers. | LitMetric

AI Article Synopsis

  • * The article explores neuromorphic classifiers using passive components like diode-resistor neurons, achieving a 95.4% accuracy in recognizing handwritten digits despite limitations in signal handling.
  • * Techniques for implementing negative weights in hardware and methods to improve fault tolerance are discussed, along with an evaluation of area, speed, and power efficiency in comparison to existing neuromorphic hardware.

Article Abstract

Bottom-up-fabricated crossbars promise superior circuit density and 3-D integrability compared with the traditional CMOS-based implementations. However, their inherent stochasticity presents difficulties in building complex circuits from components that demand precise patterning and high registration accuracies. With fewer terminals than active devices, passive components offer higher device densities and registration tolerances, making them amenable to bottom-up synthesized nanocrossbars. Motivated by this preference for passivity, we explore, in this article, neuromorphic classifiers based on passive neurons and passive synapses. We demonstrate via SPICE simulations how a shallow network of the diode-resistor-based passive rectifier neurons and resistive voltage summers, despite its inherent inability to buffer, amplify, and negate signals, can recognize MNIST digits with 95.4% accuracy. We introduce weight-to-conductance mappings that enable negative weights to be implemented in hardware without excessive memory overheads. The influences of soft and hard defects on the classification performance are evaluated, and the methods to boost fault-tolerance are proposed. The first-order evaluation of the area, speed, and power consumption of the passive multilayer perceptron classifiers is undertaken, and the results are compared with a benchmark study in neuromorphic hardware.

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Source
http://dx.doi.org/10.1109/TNNLS.2020.3016901DOI Listing

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