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arxiv: 2605.25054 · v2 · pith:EI7N7LGHnew · submitted 2026-05-24 · 💻 cs.LG · cs.AI

Scale When Needed: Adaptive Neuron-level Mixed Precision Quantization Aware Training

Ayush K. Varshney , Konstantinos Vandikas , \v{S}ar\=unas Girdzijauskas , Adam Orucu , Aneta Vulgarakis Feljan This is my paper

Pith reviewed 2026-06-30 12:21 UTC · model grok-4.3

classification 💻 cs.LG cs.AI
keywords neuron-level quantizationmixed-precision QATadaptive bit-widthquantization-aware trainingmodel compressionedge AIneural network deployment
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The pith

Each neuron learns its own bit precision during QAT to improve compression-accuracy trade-offs over layer-level methods.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes that allowing individual neurons to independently adapt their discrete precision bit-widths during quantization-aware training produces better compression with less accuracy loss than methods operating at layer or channel granularity. This matters for running deep networks on constrained edge hardware such as 6G devices, where memory footprint and inference cost must be minimized without sacrificing task performance. The approach begins at low precision and increases bit width only when training gradients indicate it is necessary, using differentiable surrogates and straight-through estimators so that the final model remains fully discrete at inference time for both weights and activations.

Core claim

NMP-QAT lets each neuron learn its own discrete precision independently during training. Bit width starts low and expands only when training signals require it, implemented through differentiable surrogates paired with straight-through estimators. The method applies to both weights and activations, preserves a fully discrete inference graph, and is shown on telecom and non-telecom datasets using MLP and tabular foundation models to deliver superior compression-accuracy results compared with existing mixed-precision QAT baselines.

What carries the argument

Per-neuron adaptive precision learning via differentiable surrogates and straight-through estimators that expand bit width on demand while keeping inference discrete.

Load-bearing premise

Differentiable surrogates combined with straight-through estimators can produce stable, generalizable discrete per-neuron precisions that translate directly into better inference performance without instability or overfitting.

What would settle it

A controlled comparison on a standard benchmark dataset where NMP-QAT at a given average bit rate yields lower accuracy than a strong layer-level mixed-precision QAT baseline.

Figures

Figures reproduced from arXiv: 2605.25054 by Adam Orucu, Aneta Vulgarakis Feljan, Ayush K. Varshney, Konstantinos Vandikas, \v{S}ar\=unas Girdzijauskas.

Figure 1
Figure 1. Figure 1: Per-layer mixed-precision distributions for weights and activations (fractions). For each layer L1 − L4, stacked bars shows the fraction of bit-widths used for weights (left) and activations (right), highlighting how precision allocation differs across layers. requires no iterative forward-backward passes at scale. PPSO is the slowest by a wide margin due to its particle swarm search, requiring up to 3840 … view at source ↗
Figure 2
Figure 2. Figure 2: Memory-utility trade-off for a 4×512 MLP. (a) Weights-only and (b) weights+activations quantization. Each point represents one method (FP32, QAT-1/1.58/4/8, PPSO, OWQ, Ours); Ours is marked by a square. The x-axis shows total theoretical memory (MiB) and the y-axis shows utility (accuracy or MSE). NMP-QAT consistently occupies the low-memory/high-utility region. (a) (b) [PITH_FULL_IMAGE:figures/full_fig_p… view at source ↗
Figure 3
Figure 3. Figure 3: Effect of model depth on NMP-QAT performance. (a) Accuracy and F1-score vs. number of layers for classification datasets. (b) Normalized MSE (relative to 1-layer baseline) for regression datasets. Deeper models consistently improve predictive quality. 13 [PITH_FULL_IMAGE:figures/full_fig_p013_3.png] view at source ↗
read the original abstract

Deploying deep neural networks on resource-constrained 6G edge devices demands aggressive compression with minimal accuracy loss. Quantization-Aware Training (QAT) has emerged as a leading compression approach; however, existing mixed-precision methods typically operate at coarse layer- or channel-level granularity. These methods often rely on heuristic or search-based bit-allocation strategies, which may overlook fine-grained variability at the neuron level. We propose Neuron-Level Mixed-Precision QAT (NMP-QAT), where each neuron independently learns its own discrete precision during training. Starting from low-bit precision, NMP-QAT expands bit-width only when training signals demand it, via differentiable surrogates and straight-through estimators, while preserving a fully discrete inference graph. This adaptability extends to both weights and activations, reducing memory movement. Evaluated on telecom and non-telecom datasets across MLP and tabular foundation model architectures, NMP-QAT achieves superior compression-accuracy trade-offs over mixed-precision QAT baselines, making it well-suited for Green AI deployments at the network edge.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 0 minor

Summary. The manuscript proposes Neuron-Level Mixed-Precision QAT (NMP-QAT), a quantization-aware training approach in which each neuron independently learns its own discrete bit-width for weights and activations. The method begins at low precision and adaptively increases bit-width only when training signals require it, using differentiable surrogates combined with straight-through estimators while maintaining a fully discrete inference graph. Experiments are described on telecom and non-telecom datasets using MLP and tabular foundation model architectures, with the central claim that NMP-QAT yields superior compression-accuracy trade-offs relative to existing mixed-precision QAT baselines.

Significance. If the empirical results hold under rigorous evaluation, the work could contribute to more efficient inference on resource-constrained edge devices by introducing neuron-level granularity to mixed-precision quantization. This finer control may reduce memory movement beyond what layer- or channel-level methods achieve and aligns with Green AI goals for 6G and similar deployments.

major comments (1)
  1. [Abstract] Abstract: the central claim that NMP-QAT achieves superior compression-accuracy trade-offs is asserted without any quantitative results, baselines, error bars, or dataset details. This absence is load-bearing because it prevents verification of whether the data actually support the claim of improved trade-offs.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their review. The single major comment concerns the abstract's lack of quantitative support for the central claim. We address this below and will revise accordingly.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim that NMP-QAT achieves superior compression-accuracy trade-offs is asserted without any quantitative results, baselines, error bars, or dataset details. This absence is load-bearing because it prevents verification of whether the data actually support the claim of improved trade-offs.

    Authors: We agree that the abstract would be strengthened by including concrete quantitative results. The full manuscript reports specific metrics (accuracy, compression ratios, and comparisons) on the telecom and tabular datasets for both MLP and foundation-model backbones, including error bars from multiple runs. In the revision we will condense the key results (e.g., average accuracy retention at given bit budgets versus layer- and channel-level mixed-precision QAT baselines) into the abstract while remaining within length limits. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation is self-contained

full rationale

The paper describes a standard QAT extension using differentiable surrogates and straight-through estimators to learn per-neuron bit-widths starting from low precision. No load-bearing step reduces by construction to its own inputs, no fitted parameter is renamed as a prediction, and no self-citation chain is invoked to justify uniqueness or an ansatz. The central claim rests on empirical evaluation across datasets and architectures rather than any self-referential derivation. This is the normal case of an independent method proposal.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review; no explicit free parameters, axioms, or invented entities are described.

pith-pipeline@v0.9.1-grok · 5738 in / 1149 out tokens · 45074 ms · 2026-06-30T12:21:33.994341+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

6 extracted references · 2 canonical work pages · 1 internal anchor

  1. [1]

    Channel-Wise Mixed-Precision Quantization for Large Language Models

    Chen, Z., Xie, B., Li, J., and Shen, C. Channel-wise mixed- precision quantization for large language models.arXiv preprint arXiv:2410.13056,

    work page internal anchor Pith review Pith/arXiv arXiv

  2. [2]

    T., Pilligundla, P., Mireshghallah, F., Yazdan- bakhsh, A., and Esmaeilzadeh, H

    Elthakeb, A. T., Pilligundla, P., Mireshghallah, F., Yazdan- bakhsh, A., and Esmaeilzadeh, H. Releq: A reinforce- ment learning approach for deep quantization of neural networks.arXiv preprint arXiv:1811.01704,

    work page arXiv

  3. [3]

    Tabular transformers for modeling multivariate time series

    Padhi, I., Schiff, Y ., Melnyk, I., Rigotti, M., Mroueh, Y ., Dognin, P., Ross, J., Nair, R., and Altman, E. Tabular transformers for modeling multivariate time series. In ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3565–3569. IEEE,

    2021

  4. [4]

    Impact Statement This paper presents NMP-QAT, a neuron-level mixed- precision quantization-aware training framework aimed at advancing efficient machine learning for resource- constrained deployments. The primary motivation is to enable accurate, low-footprint inference on edge devices, with particular relevance to 6G network intelligence, where energy ef...

    2024

  5. [5]

    However, it targetsuniformlow-bit quantization (2-8 bits) through multi-stage freezing procedures, making extension to mixed-precision settings non-trivial

    progressively quantizes neurons via freezing masks and straight-through estimators, outperforming layer- and channel-wise schemes in low-bit regimes. However, it targetsuniformlow-bit quantization (2-8 bits) through multi-stage freezing procedures, making extension to mixed-precision settings non-trivial. Neuron-levelmixed- precision methods, particularly...

    2021

  6. [6]

    8 Adaptive Neuron-level Mixed Precision QAT This proves theO(T −1/2)convergence rate for stationarity of the surrogate objective

    Choosingη= 1/ √ Tand assumingT≥L 2 so thatη≤1/L, we obtain 1 T T−1X t=0 E∥∇eL(θt)∥2 ≤ 2 eL(θ0)− eL∗ +Lσ 2 √ T . 8 Adaptive Neuron-level Mixed Precision QAT This proves theO(T −1/2)convergence rate for stationarity of the surrogate objective. It remains to relate surrogate stationarity to stationarity of the hard-quantized objective. By Assumption B.2, ∇WL...

    2012