Non-Boolean Computing Benchmarking for Beyond-CMOS Devices Based on Cellular Neural Network

  • Authors:
    Chenyun Pan (Georgia Tech), Azad J. Naeemi (Georgia Tech)
    Publication ID:
    P090126
    Publication Type:
    Paper
    Received Date:
    21-Jan-2017
    Last Edit Date:
    23-Jan-2017
    Research:
    2624.001 (Georgia Institute of Technology)

Abstract

This paper presents a uniform benchmarking methodology for non-Boolean computation based on the cellular neural network (CNN) for a variety of beyond-CMOS device technologies, including charge-based and spintronic devices. Three types of CNN implementations are investigated using analog, digital, and spintronic circuits. Monte-Carlo simulations are performed to quantify the impact of the input noise, thermal noise, and the number of bits representing the weights of synapses on the overall recall probability and delay. The results demonstrate that the recall probability improves significantly as the number of synapses increase. Using a 4-bit resolution for synapse weights provides the best trade-off between the required numbers of synapses and synapse bits for a target recall rate. Finally, three types of CNN implementations with various device technologies are benchmarked for a given input noise and recall accuracy target. It is shown that spintronic devices are promising candidates to implement CNNs, where up to 3Ă— EDP improvement is predicted in domain wall devices compared to its conventional CMOS counterpart.

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