Electrical Engineering and Computer Science Faculty Publications

Document Type

Conference Proceeding

Publication Title

Proceedings of the International Florida Artificial Intelligence Research Society Conference, FLAIRS

Abstract

The escalating energy consumption of machine learning systems demands accurate, physics-grounded efficiency measurement beyond conventional proxies like FLOPs and MACs, which fail to capture non-linear operations and memory access costs. While recent work established transistor operations (TOs) as a promising energy proxy for convolutional neural networks, this approach remains limited to a single metric and narrow architectural scope. We present TOML (Transistor Operations for Machine Learning), a framework introducing six novel metrics grounded in CMOS physics: Switching Activity Factor per Token (SAF-T), Logic State Residence Time (LSRT), Energy per Capability Unit (ECU), Memory-Compute Energy Ratio (MCER), Data-Dependent Energy Variation (DDEV), and Capability-per-Transistor-Operation (CpTO). TOML extends transistor-level energy modeling to CNNs, RNNs, LSTMs, and gradient boosting architectures through architecture-specific β-coefficients derived from fundamental semiconductor physics. Validated across seven architectures on both CPU and GPU hardware, TOML achieves r2 = 0.961 correlation with measured energy on CPU, a 49.6% improvement over FLOP-based estimation. Our metrics reveal that tested architectures are predominantly memory-bound (MCER > 1), with substantial efficiency variation within architecture families: 9.5× among accuracy-evaluated models and 6.5× among perplexity-evaluated sequence models when normalized by capability. Unlike prior approaches, TOML captures data-dependent energy variation (16–33% for CNNs) and provides capability-normalized metrics enabling fair cross-architecture comparison. © 2026, Florida Online Journals, University of Florida. All rights reserved.

DOI

10.32473/flairs.39.1.141781

Publication Date

2026

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