mlsanityData to 14 Oct 2026

End-to-end codesign of Hessian-aware quantized neural networks for FPGAs and ASICs

arXiv 2304.06745 ↗First posted 13 Apr 2023cs.LG cs.AR hep-excited by 2

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Abstract (arXiv)

We develop an end-to-end workflow for the training and implementation of co-designed neural networks (NNs) for efficient field-programmable gate array (FPGA) and application-specific integrated circuit (ASIC) hardware. Our approach leverages Hessian-aware quantization (HAWQ) of NNs, the Quantized Open Neural Network Exchange (QONNX) intermediate representation, and the hls4ml tool flow for transpiling NNs into FPGA and ASIC firmware. This makes efficient NN implementations in hardware accessible to nonexperts, in a single open-sourced workflow that can be deployed for real-time machine learning applications in a wide range of scientific and industrial settings. We demonstrate the workflow in a particle physics application involving trigger decisions that must operate at the 40 MHz collision rate of the CERN Large Hadron Collider (LHC). Given the high collision rate, all data processing must be implemented on custom ASIC and FPGA hardware within a strict area and latency. Based on these constraints, we implement an optimized mixed-precision NN classifier for high-momentum particle jets in simulated LHC proton-proton collisions.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 30 of its 49 references that are in the map. First posted on arXiv 13 Apr 2023.

Builds on

30 of 49 references.

References · 30

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  6. 2019HAWQ: Hessian AWare Quantization of Neural Networks with Mixed-Precisioncited by 402
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  10. 2019HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural Networkscited by 261
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  12. 2018Fast inference of deep neural networks in FPGAs for particle physicscited by 133
18 more
  1. 2018Mixed Precision Quantization of ConvNets via Differentiable Neural Architecture Searchcited by 125
  2. 2021MLPerf Tiny Benchmarkcited by 124
  3. 2020Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectorscited by 80
  4. 2021Nonuniform-to-Uniform Quantization: Towards Accurate Quantization via Generalized Straight-Through Estimationcited by 79
  5. 2021ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed Trainingcited by 61
  6. 2020HAWQV3: Dyadic Neural Network Quantizationcited by 60
  7. 2018FINN-R: An End-to-End Deep-Learning Framework for Fast Exploration of Quantized Neural Networkscited by 56
  8. 2020A Statistical Framework for Low-bitwidth Training of Deep Neural Networkscited by 51
  9. 2020Compressing deep neural networks on FPGAs to binary and ternary precision with HLS4MLcited by 42
  10. 2021Fast convolutional neural networks on FPGAs with hls4mlcited by 42
  11. 2021Applications and Techniques for Fast Machine Learning in Sciencecited by 32
  12. 2021Ps and Qs: Quantization-aware pruning for efficient low latency neural network inferencecited by 31
  13. 2021HAO: Hardware-aware neural Architecture Optimization for Efficient Inferencecited by 16
  14. 2022FastML Science Benchmarks: Accelerating Real-Time Scientific Edge Machine Learningcited by 12
  15. 2022Real-time semantic segmentation on FPGAs for autonomous vehicles with hls4mlcited by 11
  16. 2022QONNX: Representing Arbitrary-Precision Quantized Neural Networkscited by 10
  17. 2018On Periodic Functions as Regularizers for Quantization of Neural Networkscited by 8
  18. 2020HCM: Hardware-Aware Complexity Metric for Neural Network Architecturescited by 3

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Cited by year

Citing papers per year: 2024: 1; 2025: 1.

2 papers.

  1. 2024Automated and Holistic Co-design of Neural Networks and ASICs for Enabling In-Pixel Intelligencecited by 0
  2. 2025SIRA: Scaled-Integer Range Analysis for Optimizing FPGA Dataflow Neural Network Acceleratorscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024HGQ: High Granularity Quantization for Real-time Neural Networks on FPGAs
  2. 2021A reconfigurable neural network ASIC for detector front-end data compression at the HL-LHC
  3. 2025JEDI-linear: Fast and Efficient Graph Neural Networks for Jet Tagging on FPGAs
  4. 2025Hardware-Aware Neural Network Compilation with Learned Optimization: A RISC-V Accelerator Approach
  5. 2025Quantization Effects of Artificial Neural Networks for Embedded Edge-Computing Applications
  6. 2023Quantized Neural Networks for Low-Precision Accumulation with Guaranteed Overflow Avoidance
  7. 2025Neural Architecture Codesign for Fast Physics Applications
  8. 2021Automated Backend-Aware Post-Training Quantization