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arXiv · 2206.00250

Time-multiplexed In-memory computation scheme for mapping Quantized Neural Networks on hybrid CMOS-OxRAM building blocks

Sandeep Kaur Kingra·Vivek Parmar·Manoj Sharma·Manan Suri
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Abstract

In this work, we experimentally demonstrate two key building blocks for realizing Binary/Ternary Neural Networks (BNNs/TNNs): (i) 130 nm CMOS based sigmoidal neurons and (ii) HfOx based multi-level (MLC) OxRAM-synaptic blocks. An optimized vector matrix multiplication programming scheme that utilizes the two building blocks is also presented. Compared to prior approaches that utilize differential synaptic structures, a single device per synapse with two sets of READ operations is used. Proposed hardware mapping strategy shows performance change of <5% (decrease of 2-5% for TNN, increase of 0.2% for BNN) compared to ideal quantized neural networks (QNN) with significant memory savings in the order of 16-32x for classification problem on Fashion MNIST (FMNIST) dataset. Impact of OxRAM device variability on the performance of Hardware QNN (BNN/TNN) is also analyzed.

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Sandeep Kaur Kingra, Vivek Parmar, Manoj Sharma, Manan Suri. 2022-07-28. Time-multiplexed In-memory computation scheme for mapping Quantized Neural Networks on hybrid CMOS-OxRAM building blocks. https://doi.org/10.1109/tnano.2022.3193921

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