Optimizing for In-memory Deep Learning with Emerging Memory Technology

TL;DR

This paper presents three optimization techniques to address the instability of emerging memory technology in in-memory deep learning, significantly improving accuracy and energy efficiency.

Contribution

It introduces novel mathematical optimization methods that mitigate data instability issues in emerging memory tech for in-memory deep learning.

Findings

Achieves near state-of-the-art accuracy with unstable memory technology.

At least tenfold increase in energy efficiency over existing methods.

Successfully recovers model accuracy despite memory fluctuations.

Abstract

In-memory deep learning computes neural network models where they are stored, thus avoiding long distance communication between memory and computation units, resulting in considerable savings in energy and time. In-memory deep learning has already demonstrated orders of magnitude higher performance density and energy efficiency. The use of emerging memory technology promises to increase the gains in density, energy, and performance even further. However, emerging memory technology is intrinsically unstable, resulting in random fluctuations of data reads. This can translate to non-negligible accuracy loss, potentially nullifying the gains. In this paper, we propose three optimization techniques that can mathematically overcome the instability problem of emerging memory technology. They can improve the accuracy of the in-memory deep learning model while maximizing its energy efficiency. Experiments show that our solution can fully recover most models' state-of-the-art accuracy, and achieves at least an order of magnitude higher energy efficiency than the state-of-the-art.