Predictive first-principles simulations for co-designing next-generation energy-efficient AI systems

TL;DR

This paper advocates for the use of predictive, first-principles simulations to co-design energy-efficient AI hardware, focusing on device and interconnect optimization to significantly reduce energy consumption in generative AI workloads.

Contribution

It introduces a novel approach using physics-based simulations to guide the co-design of AI hardware components for enhanced energy efficiency.

Findings

Predictive simulations can identify device regimes for energy savings.

Co-design across materials, devices, and architectures is essential.

Potential for orders-of-magnitude improvements in AI hardware energy efficiency.

Abstract

In modern generative-AI workloads, matrix-vector/matrix-matrix multiplications (\emph{MatMul}) dominate the compute and energy cost. Achieving dramatic reductions in energy per token therefore requires a novel, specialized hardware that is co-designed across materials, devices, interconnects, circuits, and architectures rather than optimized at any single layer in isolation. In this \emph{Perspectives} article, we argue that \emph{predictive} (first-principles, fitting-parameter-free) device and interconnect simulations can close the loop between nanoscale physics and workload-level metrics, enabling the identification of device/interconnect operating regimes that plausibly support \emph{orders-of-magnitude} improvements in energy efficiency of AI accelerators.