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Stochastic Rounding Improves Low-Precision Transformer Inference

By PulseAugur Editorial · [1 sources] ·

Researchers have investigated the use of stochastic rounding (SR) versus round-to-nearest (RN) for low-precision transformer inference, finding that the optimal method depends on the specific layer within the model. They developed a variable-precision stochastic rounding (VPSR) algorithm to enable experiments at arbitrary precisions. Their analysis revealed that SR's error envelope grows slower than RN's, particularly in multilayer perceptrons (MLPs), while RN is more suitable for the language model head. By strategically applying SR to MLPs and RN to the head, they achieved a perplexity close to full-precision on DistilGPT-2. AI

IMPACT Optimizing low-precision inference could lead to more efficient deployment of large language models on resource-constrained hardware.

RANK_REASON Academic paper detailing a novel algorithm and experimental findings on optimizing transformer inference. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Stochastic Rounding Improves Low-Precision Transformer Inference

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Academic paper detailing a novel algorithm and experimental findings on optimizing transformer inference. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Yohan Chatelain (Krembil Centre for Neuroinformatics, CAMH, Toronto, Canada), Pablo de Oliveira Castro (Universite Paris-Saclay, UVSQ, LI-PaRAD, Versailles, France) ·

    Stochastic Rounding in Low-Precision Transformer Inference: A Variable-Precision Emulation Study of a Small GPT-2

    arXiv:2610.01889v1 Announce Type: new Abstract: Should low-precision transformer inference use stochastic rounding (SR) or round-to-nearest (RN)? The answer depends on where in the network you look. We isolate this effect by holding the numerical format fixed and varying only the…

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