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arXiv · 2204.09918

MRAM-based Analog Sigmoid Function for In-memory Computing

Md Hasibul Amin·Mohammed Elbtity·Mohammadreza Mohammadi·Ramtin Zand
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Abstract

We propose an analog implementation of the transcendental activation function leveraging two spin-orbit torque magnetoresistive random-access memory (SOT-MRAM) devices and a CMOS inverter. The proposed analog neuron circuit consumes 1.8-27x less power, and occupies 2.5-4931x smaller area, compared to the state-of-the-art analog and digital implementations. Moreover, the developed neuron can be readily integrated with memristive crossbars without requiring any intermediate signal conversion units. The architecture-level analyses show that a fully-analog in-memory computing (IMC) circuit that use our SOT-MRAM neuron along with an SOT-MRAM based crossbar can achieve more than 1.1x, 12x, and 13.3x reduction in power, latency, and energy, respectively, compared to a mixed-signal implementation with analog memristive crossbars and digital neurons. Finally, through cross-layer analyses, we provide a guide on how varying the device-level parameters in our neuron can affect the accuracy of multilayer perceptron (MLP) for MNIST classification.

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Md Hasibul Amin, Mohammed Elbtity, Mohammadreza Mohammadi, Ramtin Zand. 2022-04-21. MRAM-based Analog Sigmoid Function for In-memory Computing. https://doi.org/10.1145/3526241.3530376

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