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HUXIN: In-Memory Crossbar Core for Integration of Biologically Inspired Stochastic Neuron Models
  • +1
  • Louis Primeau,
  • Xuening Dong,
  • Amirali Amirsoleimani,
  • Roman Genov
Louis Primeau
Department of Electrical and Computer Engineering, University of Toronto
Xuening Dong
Department of Electrical and Computer Engineering, University of Toronto
Amirali Amirsoleimani
Department of Electrical Engineering and Computer Science, York University

Corresponding Author:[email protected]

Author Profile
Roman Genov
Department of Electrical and Computer Engineering, University of Toronto

Abstract

In this work, we solve nonlinear systems of ordinary differential equations coupled to noisy forcing, commonly used for models of neurons such as the Hodgkin-Huxley equation, over a memristor crossbar based computing system. We demonstrate stability and faithfulness of the distributions even under the effects of nonidealities of the memristors and the system itself. We investigate the properties of the dynamical systems under quantization faithfulness, varying the level of precision of the fixed point integer representation and concluding that 24 bits is enough for solution of the Hodgkin-Huxley equations, demonstrating that our solver can operate with both high precision and achieve speedups with low precision approximate computation.