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arxiv: 2410.03294 · v4 · submitted 2024-10-04 · 💻 cs.LG

Resource-aware Mixed-precision Quantization for Enhancing Deployability of Transformers for Time-series Forecasting on Embedded FPGAs

Tianheng Ling , Chao Qian , Gregor Schiele This is my paper

Pith reviewed 2026-05-23 19:49 UTC · model grok-4.3

classification 💻 cs.LG
keywords mixed-precision quantizationTransformerstime-series forecastingembedded FPGAsresource utilization estimatesVHDL templatemodel deploymentinteger quantization
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The pith

Resource-aware mixed-precision quantization enables deployment of five previously non-deployable Transformer configurations on embedded FPGAs.

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

The paper develops a method to make integer-only quantized Transformers deployable on resource-limited FPGAs for time-series forecasting. It modifies a VHDL template to use BRAM efficiently for intermediate results across layers and introduces a resource-aware mixed-precision quantization technique. This allows exploration of quantization strategies without deep Neural Architecture Search knowledge. The estimates of resource use match actual deployment within 3 percent. As a result, models that could not deploy under uniform bit widths now run successfully.

Core claim

By introducing a selectable resource type in the VHDL template for storing intermediate results and developing a resource-aware mixed-precision quantization approach, the method enables deployment of mixed-precision quantized Transformers on embedded FPGAs, succeeding where uniform quantization failed in five cases, while providing resource utilization estimates with a discrepancy as low as 3 percent compared to actual metrics.

What carries the argument

The resource-aware mixed-precision quantization approach that estimates hardware resource utilization to select per-layer bit widths and breaks deployment bottlenecks via BRAM usage in the enhanced VHDL template.

If this is right

  • Mixed-precision configurations become deployable on constrained FPGAs where uniform bit widths previously prevented deployment.
  • Researchers without extensive Neural Architecture Search expertise can explore hardware-level quantization strategies for Transformers.
  • Resource estimates match actual deployment metrics closely enough to guide design choices reliably.
  • Transformers for time-series forecasting gain broader applicability in embedded edge systems.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same template enhancement and estimation method could support other sequence models beyond Transformers if adapted to their layer structures.
  • The approach reduces the need for repeated physical deployments during design iteration by providing early accurate predictions.
  • Layer-specific bit-width choices guided by resources may extend to power or latency optimization targets on the same hardware.

Load-bearing premise

The resource utilization estimates produced by the mixed-precision method remain accurate enough for deployment decisions when applied to new model configurations or different FPGA boards.

What would settle it

A deployment of a new mixed-precision configuration on the target FPGA where the measured resource utilization after implementation differs by substantially more than 3 percent from the method's estimate.

Figures

Figures reproduced from arXiv: 2410.03294 by Chao Qian, Gregor Schiele, Tianheng Ling.

Figure 1
Figure 1. Figure 1: The Architecture of the Transformer Model [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Uniform (left) vs Mixed (right) 8-bit Linear Layer [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 4
Figure 4. Figure 4: Uniform (up) vs Mixed (down) 8-bit FFN Module [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Workflow of Resource-aware Mixed-precision Quantization [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: LUTs Utilization Cross Key Components for [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Distribution of Candidate Mixed-Precision Bitwidth Combinations [PITH_FULL_IMAGE:figures/full_fig_p015_7.png] view at source ↗
read the original abstract

This study addresses the deployment challenges of integer-only quantized Transformers on resource-constrained embedded FPGAs (Xilinx Spartan-7 XC7S15). We enhanced the flexibility of our VHDL template by introducing a selectable resource type for storing intermediate results across model layers, thereby breaking the deployment bottleneck by utilizing BRAM efficiently. Moreover, we developed a resource-aware mixed-precision quantization approach that enables researchers to explore hardware-level quantization strategies without requiring extensive expertise in Neural Architecture Search. This method provides accurate resource utilization estimates with a precision discrepancy as low as 3%, compared to actual deployment metrics. Compared to previous work, our approach has successfully facilitated the deployment of model configurations utilizing mixed-precision quantization, thus overcoming the limitations inherent in five previously non-deployable configurations with uniform quantization bitwidths. Consequently, this research enhances the applicability of Transformers in embedded systems, facilitating a broader range of Transformer-powered applications on edge devices.

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

2 major / 0 minor

Summary. The paper claims to address deployment challenges of integer-only quantized Transformers on embedded FPGAs (Xilinx Spartan-7 XC7S15) by enhancing a VHDL template with selectable resource type for BRAM usage to break deployment bottlenecks, and by introducing a resource-aware mixed-precision quantization method that provides resource estimates with as low as 3% discrepancy from actual deployment. It reports successfully deploying five model configurations using mixed-precision quantization that were previously non-deployable under uniform quantization bitwidths.

Significance. If the central attribution holds and the estimates prove reliable across configurations, the work could practically expand Transformer deployment on resource-constrained FPGAs by reducing the need for expert NAS and providing actionable hardware-level quantization guidance.

major comments (2)
  1. [Abstract] Abstract: the claim that the mixed-precision approach 'overcame the limitations inherent in five previously non-deployable configurations with uniform quantization bitwidths' cannot be evaluated because the manuscript provides no control experiment re-testing those five configurations with uniform quantization after the VHDL template update that enables BRAM; the deployment success may be attributable to the template change rather than the quantization method.
  2. [Abstract] Abstract: the statement that resource utilization estimates have 'a precision discrepancy as low as 3%' is presented without any description of the measurement protocol, the set of models or layers compared, or the definition of 'actual deployment metrics,' rendering the accuracy claim unverifiable and limiting assessment of its utility for new configurations.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the insightful comments, which highlight areas where the abstract claims require stronger supporting evidence. We address each point below and will revise the manuscript accordingly to improve clarity and verifiability.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim that the mixed-precision approach 'overcame the limitations inherent in five previously non-deployable configurations with uniform quantization bitwidths' cannot be evaluated because the manuscript provides no control experiment re-testing those five configurations with uniform quantization after the VHDL template update that enables BRAM; the deployment success may be attributable to the template change rather than the quantization method.

    Authors: We agree that the current presentation does not isolate the contribution of mixed-precision quantization from the VHDL template update. The template enhancement (selectable BRAM resource type) is a foundational enabler, but the resource-aware mixed-precision method is intended to provide the additional flexibility needed for the five configurations. To address this, the revised manuscript will include a control experiment re-testing the five configurations under uniform quantization bitwidths using the updated VHDL template, confirming they remain non-deployable due to resource limits. This will strengthen the attribution to the mixed-precision approach. revision: yes

  2. Referee: [Abstract] Abstract: the statement that resource utilization estimates have 'a precision discrepancy as low as 3%' is presented without any description of the measurement protocol, the set of models or layers compared, or the definition of 'actual deployment metrics,' rendering the accuracy claim unverifiable and limiting assessment of its utility for new configurations.

    Authors: We acknowledge that the abstract does not provide sufficient methodological detail to evaluate the 3% discrepancy claim. The full manuscript contains the underlying comparisons, but these are not summarized in the abstract. In the revision, we will expand the abstract to briefly describe the measurement protocol, the specific models/layers evaluated, and the definition of actual deployment metrics (post-synthesis resource utilization on the target FPGA). A new subsection will also be added to the main text for full transparency, enabling readers to assess the estimate reliability for new configurations. revision: yes

Circularity Check

0 steps flagged

No circularity; empirical deployment results are independent of inputs

full rationale

The paper presents an empirical claim of successful FPGA deployments for five mixed-precision configurations after a VHDL template update to support BRAM. No equations, parameter fits, predictions, or self-citations are shown that reduce any result to its own inputs by construction. The 3% discrepancy figure and deployment outcomes are reported as measured quantities, not derived tautologically from the method itself. The central claim therefore remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no equations, parameters, or explicit assumptions; ledger left minimal.

pith-pipeline@v0.9.0 · 5691 in / 1018 out tokens · 16306 ms · 2026-05-23T19:49:04.245911+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

21 extracted references · 21 canonical work pages · 1 internal anchor

  1. [1]

    Artificial intelligence-based-based Internet of Things sys- tems pp

    Chander, B., Pal, S., De, D., Buyya, R.: Artificial Intelligence-based Internet of Things for industry 5.0. Artificial intelligence-based-based Internet of Things sys- tems pp. 3–45 (2022)

    work page 2022

  2. [2]

    Proceedings of the IEEE 107(8), 1738–1762 (2019)

    Zhou, Z., Chen, X., Li, E., Zeng, L., Luo, K., Zhang, J.: Edge intelligence: paving the last mile of Artificial Intelligence with edge computing. Proceedings of the IEEE 107(8), 1738–1762 (2019)

    work page 2019

  3. [3]

    Journal of Computer Sciences and Applications 9(1), 23–34 (2021)

    Dave, R., Seliya, N., Siddiqui, N., Mao, S.: The benefits of edge computing in healthcare, smart cities, and IoT. Journal of Computer Sciences and Applications 9(1), 23–34 (2021)

    work page 2021

  4. [4]

    In: Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence

    Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: a survey. In: Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence. IJCAI ’23 (2023)

    work page 2023

  5. [5]

    Mobile Information Sys- tems 2020, 1–19 (2020) 20 T

    Chen, C., Zhang, P., Zhang, H., Dai, J., Yi, Y., Zhang, H., Zhang, Y.: Deep Learn- ing on computational-resource-limited platforms: a survey. Mobile Information Sys- tems 2020, 1–19 (2020) 20 T. Ling et al

    work page 2020

  6. [6]

    Internet of Things 19, 100514 (2022)

    Gill, S.S., Xu, M., Ottaviani, C., Patros, P., Bahsoon, R., Shaghaghi, A., Golec, M., Stankovski, V., Wu, H., Abraham, A., et al.: AI for next generation computing: emerging trends and future directions. Internet of Things 19, 100514 (2022)

    work page 2022

  7. [7]

    Electronics 10(8), 895 (2021)

    Seng, K.P., Lee, P.J., Ang, L.M.: Embedded intelligence on FPGA: survey, appli- cations and challenges. Electronics 10(8), 895 (2021)

    work page 2021

  8. [8]

    In: 2024 IEEE Annual Congress on Artificial Intelligence of Things (AIoT)

    Ling, T., Qian, C., Schiele, G.: Integer-only quantized Transformers for embedded FPGA-based time-series forecasting in AIoT. In: 2024 IEEE Annual Congress on Artificial Intelligence of Things (AIoT). pp. 38–44. IEEE (2024)

    work page 2024

  9. [9]

    In: Joint European Confer- ence on Machine Learning and Knowledge Discovery in Databases

    Qian, C., Ling, T., Schiele, G.: Enhancing energy-efficiency by solving the through- put bottleneck of LSTM cells for embedded FPGAs. In: Joint European Confer- ence on Machine Learning and Knowledge Discovery in Databases. pp. 594–605. Springer (2022)

    work page 2022

  10. [10]

    In: 2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS)

    Becnel, T., Kelly, K., Gaillardon, P.E.: Tiny time-series Transformers: realtime multi-target sensor inference at the edge. In: 2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS). pp. 1–6. IEEE (2022)

    work page 2022

  11. [11]

    A White Paper on Neural Network Quantization

    Nagel, M., Fournarakis, M., Amjad, R.A., Bondarenko, Y., Van Baalen, M., Blankevoort, T.: A white paper on Neural Network quantization. arXiv preprint arXiv:2106.08295 (2021)

    work page internal anchor Pith review Pith/arXiv arXiv 2021

  12. [12]

    IEEE Transactions on Pattern Analysis and Machine Intelligence (2024)

    Rakka, M., Fouda, M.E., Khargonekar, P., Kurdahi, F.: A review of state-of-the- art mixed-precision neural network frameworks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2024)

    work page 2024

  13. [13]

    arXiv preprint arXiv:2203.15952 (2022)

    Ding, S., Meadowlark, P., He, Y., Lew, L., Agrawal, S., Rybakov, O.: 4-bit Conformer with native quantization aware training for speech recognition. arXiv preprint arXiv:2203.15952 (2022)

    work page arXiv 2022

  14. [14]

    Ad- vances in neural information processing systems 33, 18518–18529 (2020)

    Dong, Z., Yao, Z., Arfeen, D., Gholami, A., Mahoney, M.W., Keutzer, K.: HAWQ- V2: Hessian aware trace-weighted quantization of Deep Neural Networkss. Ad- vances in neural information processing systems 33, 18518–18529 (2020)

    work page 2020

  15. [15]

    In: 2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)

    Qian, C., Ling, T., Schiele, G.: ElasticAI: Creating and deploying energy-efficient Deep Learning accelerator for pervasive computing. In: 2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops). pp. 297–299. IEEE (2023)

    work page 2023

  16. [16]

    Journal of Systems Ar- chitecture p

    Chitty-Venkata, K.T., Mittal, S., Emani, M., Vishwanath, V., Somani, A.K.: A survey of techniques for optimizing Transformer inference. Journal of Systems Ar- chitecture p. 102990 (2023)

    work page 2023

  17. [17]

    In: Proceedings of the Thirty-Third International Joint Con- ference on Artificial Intelligence

    Zhang, X., Chowdhury, R.R., Gupta, R.K., Shang, J.: Large language models for time series: a survey. In: Proceedings of the Thirty-Third International Joint Con- ference on Artificial Intelligence. IJCAI ’24 (2024)

    work page 2024

  18. [18]

    arXiv preprint arXiv:2402.05830 (2024)

    Zhao, Y., Zhou, T., Chen, C., Sun, L., Qian, Y., Jin, R.: Sparse-VQ Transformer: an FFN-free framework with vector quantization for enhanced time series forecasting. arXiv preprint arXiv:2402.05830 (2024)

    work page arXiv 2024

  19. [19]

    arXiv preprint arXiv:2401.02721 (2024)

    Okubo, I., Sugiura, K., Matsutani, H.: A cost-efficient FPGA implementation of tiny Transformer model using neural ODE. arXiv preprint arXiv:2401.02721 (2024)

    work page arXiv 2024

  20. [20]

    In: Proceedings of the IEEE/CVF international conference on computer vision

    Chang, S.E., Li, Y., Sun, M., Jiang, W., Liu, S., Wang, Y., Lin, X.: RMSMP: A novel Deep Neural Networks quantization framework with row-wise mixed schemes and multiple precisions. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 5251–5260 (2021)

    work page 2021

  21. [21]

    In: 2022 32nd Interna- tional Conference on Field-Programmable Logic and Applications (FPL)

    Li, Z., Sun, M., Lu, A., Ma, H., Yuan, G., Xie, Y., Tang, H., Li, Y., Leeser, M., Wang, Z., et al.: Auto-vit-acc: An FPGA-aware automatic acceleration framework for vision Transformer with mixed-scheme quantization. In: 2022 32nd Interna- tional Conference on Field-Programmable Logic and Applications (FPL). pp. 109–

    work page 2022