GENIAL: Generative Design Space Exploration via Network Inversion for Low Power Algorithmic Logic Units

TL;DR

GENIAL is a machine learning framework that automates the design of low-power arithmetic units, especially multipliers, by efficiently exploring the design space and optimizing for power savings using a Transformer-based surrogate model.

Contribution

It introduces a novel Transformer-based surrogate model and a model inversion technique for automatic, efficient optimization of arithmetic units in digital systems.

Findings

Achieves up to 18% switching activity savings in multipliers.

Demonstrates faster convergence and higher sample efficiency than existing methods.

Shows versatility by improving other logic functions like Finite State Machines.

Abstract

As AI workloads proliferate, optimizing arithmetic units is becoming increasingly important for reducing the footprint of digital systems. Conventional design flows, which often rely on manual or heuristic-based optimization, are limited in their ability to thoroughly explore the vast design space. In this paper, we introduce GENIAL, a machine learning-based framework for the automatic generation and optimization of arithmetic units, with a focus on multipliers. At the core of GENIAL is a Transformer-based surrogate model trained in two stages, involving self-supervised pretraining followed by supervised finetuning, to robustly forecast key hardware metrics such as power and area from abstracted design representations. By inverting the surrogate model, GENIAL efficiently searches for new operand encodings that directly minimize power consumption in arithmetic units for specific input data distributions. Extensive experiments on large datasets demonstrate that GENIAL is consistently more sample efficient than other methods, and converges faster towards optimized designs. This enables deployment of a high-effort logic synthesis optimization flow in the loop, improving the accuracy of the surrogate model. Notably, GENIAL automatically discovers encodings that achieve up to 18% switching activity savings within multipliers on representative AI workloads compared with the conventional two's complement. We also demonstrate the versatility of our approach by achieving significant improvements on Finite State Machines, highlighting GENIAL's applicability for a wide spectrum of logic functions. Together, these advances mark a significant step toward automated Quality-of-Results-optimized combinational circuit generation for digital systems.