Solving the compute crisis with physics-based ASICs

TL;DR

Physics-based ASICs leverage intrinsic physical dynamics for computation, offering a transformative approach to address the AI compute crisis by significantly improving energy efficiency and throughput, and enabling novel co-design strategies.

Contribution

This paper introduces a paradigm shift by relaxing traditional ASIC constraints, utilizing physical processes for computation, and proposing a new co-design approach for AI and scientific workloads.

Findings

Physics-based ASICs can improve energy efficiency in AI workloads.

They enable novel co-design strategies aligning algorithms with physical primitives.

Potential to accelerate AI applications and scientific simulations.

Abstract

Escalating artificial intelligence (AI) demands expose a critical "compute crisis" characterized by unsustainable energy consumption, prohibitive training costs, and the approaching limits of conventional CMOS scaling. Physics-based Application-Specific Integrated Circuits (ASICs) present a transformative paradigm by directly harnessing intrinsic physical dynamics for computation rather than expending resources to enforce idealized digital abstractions. By relaxing the constraints needed for traditional ASICs, like enforced statelessness, unidirectionality, determinism, and synchronization, these devices aim to operate as exact realizations of physical processes, offering substantial gains in energy efficiency and computational throughput. This approach enables novel co-design strategies, aligning algorithmic requirements with the inherent computational primitives of physical systems. Physics-based ASICs could accelerate critical AI applications like diffusion models, sampling, optimization, and neural network inference as well as traditional computational workloads like scientific simulation of materials and molecules. Ultimately, this vision points towards a future of heterogeneous, highly-specialized computing platforms capable of overcoming current scaling bottlenecks and unlocking new frontiers in computational power and efficiency.