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14 October 2024 |
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Article overview
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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
; | | Date: |
1 Jun 2022 | | 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. | | Source: | arXiv, 2206.00250 | | Services: | Forum | Review | PDF | Favorites | |
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