Back to Bits: Extending Shannon's communication performance framework to computing

TL;DR

This paper extends Shannon's information theory to define a new, more comprehensive performance metric for computing systems, capturing the meaningful information processed beyond traditional measures like flops.

Contribution

It introduces a novel information-theoretic framework for evaluating computing performance, applicable across diverse and emerging computing paradigms.

Findings

Defines computing as information transformation via mutual information

Provides a unified, implementation-agnostic performance measure

Applicable to analog, quantum, and reversible computing

Abstract

This work proposes a novel computing performance unit grounded in information theory. Modern computing systems are increasingly diverse, supporting low-precision formats, hardware specialization, and emerging paradigms such as analog, quantum, and reversible logic. Traditional metrics like floating-point operations (flops) no longer accurately capture this complexity. We frame computing as the transformation of information through a channel and define performance in terms of the mutual information between a system's inputs and outputs. This approach measures not just the quantity of data processed, but the amount of meaningful information encoded, manipulated, and retained through computation. Our framework provides a principled, implementation-agnostic foundation for evaluating performance.