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arXiv · 2203.06223

Generalized Key-Value Memory to Flexibly Adjust Redundancy in Memory-Augmented Networks

Denis Kleyko·Geethan Karunaratne·Jan M. Rabaey·Abu Sebastian·Abbas Rahimi
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Abstract

Memory-augmented neural networks enhance a neural network with an external key-value memory whose complexity is typically dominated by the number of support vectors in the key memory. We propose a generalized key-value memory that decouples its dimension from the number of support vectors by introducing a free parameter that can arbitrarily add or remove redundancy to the key memory representation. In effect, it provides an additional degree of freedom to flexibly control the trade-off between robustness and the resources required to store and compute the generalized key-value memory. This is particularly useful for realizing the key memory on in-memory computing hardware where it exploits nonideal, but extremely efficient non-volatile memory devices for dense storage and computation. Experimental results show that adapting this parameter on demand effectively mitigates up to 44% nonidealities, at equal accuracy and number of devices, without any need for neural network retraining.

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Denis Kleyko, Geethan Karunaratne, Jan M. Rabaey, Abu Sebastian, Abbas Rahimi. 2022-03-11. Generalized Key-Value Memory to Flexibly Adjust Redundancy in Memory-Augmented Networks. https://doi.org/10.1109/tnnls.2022.3159445

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