Emerging Nonvolatile Memories for Machine Learning

TL;DR

Emerging nonvolatile memories, especially memristive crossbar arrays, offer promising solutions for accelerating machine learning tasks by enabling analogue in-memory computing, addressing current computational bottlenecks.

Contribution

This paper explores the application of emerging nonvolatile memories for implementing analogue in-memory computing systems tailored for machine learning, highlighting technological challenges and solutions.

Findings

Memristive crossbar arrays can significantly accelerate neural network computations.

Technological challenges include device variability and integration issues.

Potential for nonvolatile memories to complement or replace traditional computing architectures.

Abstract

Digital computers have been getting exponentially faster for decades, but huge challenges exist today. Transistor scaling, described by Moore's law, has been slowing down over the last few years, ending the era of fully predictable performance improvements. Furthermore, the data-centric computing demands fueled by machine learning applications are rapidly growing, and current computing systems -- even with the historical rate of improvements driven by Moore's law -- cannot keep up with these enormous computational demands. Some are turning to analogue in-memory computing as a solution, where specialised systems operating on physical principles accelerate specific tasks. We explore how emerging nonvolatile memories can be used to implement such systems tailored for machine learning. In particular, we discuss how memristive crossbar arrays can accelerate key linear algebra operations used in neural networks, what technological challenges remain, and how they can be overcome.