Neuromorphic Computing for Low-Power Artificial Intelligence

TL;DR

This paper discusses neuromorphic computing as a promising approach to overcome energy efficiency limits in AI by leveraging novel devices, architectures, and algorithms inspired by the brain.

Contribution

It surveys the limitations of classical CMOS technology and outlines how cross-layer neuromorphic approaches can address these challenges.

Findings

Neuromorphic computing offers potential energy efficiency improvements for AI.

Cross-layer co-design is essential for realizing neuromorphic systems.

Novel device and circuit innovations are crucial for scalable neuromorphic AI.

Abstract

Classical computing is beginning to encounter fundamental limits of energy efficiency. This presents a challenge that can no longer be solved by strategies such as increasing circuit density or refining standard semiconductor processes. The growing computational and memory demands of artificial intelligence (AI) require disruptive innovation in how information is represented, stored, communicated, and processed. By leveraging novel device modalities and compute-in-memory (CIM), in addition to analog dynamics and sparse communication inspired by the brain, neuromorphic computing offers a promising path toward improvements in the energy efficiency and scalability of current AI systems. But realizing this potential is not a matter of replacing one chip with another; rather, it requires a co-design effort, spanning new materials and non-volatile device structures, novel mixed-signal circuits and architectures, and learning algorithms tailored to the physics of these substrates. This article surveys the key limitations of classical complementary metal-oxide-semiconductor (CMOS) technology and outlines how such cross-layer neuromorphic approaches may overcome them.