mlsanityData to 7 Oct 2026

hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware

arXiv 2512.01463 ↗First posted 1 Dec 2025cs.AR cs.LG hep-excited by 8

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Abstract (arXiv)

We present hls4ml, a free and open-source platform that translates machine learning (ML) models from modern deep learning frameworks into high-level synthesis (HLS) code that can be integrated into full designs for field-programmable gate arrays (FPGAs) or application-specific integrated circuits (ASICs). With its flexible and modular design, hls4ml supports a large number of deep learning frameworks and can target HLS compilers from several vendors, including Vitis HLS, Intel oneAPI and Catapult HLS. Together with a wider eco-system for software-hardware co-design, hls4ml has enabled the acceleration of ML inference in a wide range of commercial and scientific applications where low latency, resource usage, and power consumption are critical. In this paper, we describe the structure and functionality of the hls4ml platform. The overarching design considerations for the generated HLS code are discussed, together with selected performance results.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 45 of its 135 references that are in the map. First posted on arXiv 1 Dec 2025.

Builds on

45 of 135 references.

References · 45

  1. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,571
  2. 2019Once-for-All: Train One Network and Specialize it for Efficient Deploymentcited by 778
  3. 2022Tutel: Adaptive Mixture-of-Experts at Scalecited by 199
  4. 2016FINN: A Framework for Fast, Scalable Binarized Neural Network Inferencecited by 141
  5. 2018Fast inference of deep neural networks in FPGAs for particle physicscited by 132
  6. 2020Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectorscited by 78
  7. 2020LogicNets: Co-Designed Neural Networks and Circuits for Extreme-Throughput Applicationscited by 55
  8. 2018FINN-R: An End-to-End Deep-Learning Framework for Fast Exploration of Quantized Neural Networkscited by 54
  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. 2020Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physicscited by 34
  12. 2021Applications and Techniques for Fast Machine Learning in Sciencecited by 32
33 more
  1. 2021Graph Neural Networks for Charged Particle Tracking on FPGAscited by 17
  2. 2022Ultra-low latency recurrent neural network inference on FPGAs for physics applications with hls4mlcited by 14
  3. 2024Ultrafast jet classification on FPGAs for the HL-LHCcited by 12
  4. 2024Locality-Sensitive Hashing-Based Efficient Point Transformer with Applications in High-Energy Physicscited by 12
  5. 2021Enabling Binary Neural Network Training on the Edgecited by 11
  6. 2022Real-time semantic segmentation on FPGAs for autonomous vehicles with hls4mlcited by 11
  7. 2022QONNX: Representing Arbitrary-Precision Quantized Neural Networkscited by 9
  8. 2022Open-source FPGA-ML codesign for the MLPerf Tiny Benchmarkcited by 9
  9. 2023Symbolic Regression on FPGAs for Fast Machine Learning Inferencecited by 9
  10. 2024HGQ: High Granularity Quantization for Real-time Neural Networks on FPGAscited by 9
  11. 2025Fast Jet Tagging with MLP-Mixers on FPGAscited by 9
  12. 2025PolyLUT: Ultra-low Latency Polynomial Inference with Hardware-Aware Structured Pruningcited by 8
  13. 2025End-to-end workflow for machine learning-based qubit readout with QICK and hls4mlcited by 8
  14. 2025da4ml: Distributed Arithmetic for Real-time Neural Networks on FPGAscited by 8
  15. 2023FPGA Resource-aware Structured Pruning for Real-Time Neural Networkscited by 7
  16. 2023Low latency optical-based mode tracking with machine learning deployed on FPGAs on a tokamakcited by 7
  17. 2020HPIPE: Heterogeneous Layer-Pipelined and Sparse-Aware CNN Inference for FPGAscited by 6
  18. 2024SymbolNet: Neural Symbolic Regression with Adaptive Dynamic Pruning for Compressioncited by 6
  19. 2025Sub-microsecond Transformers for Jet Tagging on FPGAscited by 6
  20. 2020Agile SoC Development with Open ESPcited by 5
  21. 2024Field-Programmable Gate Array Architecture for Deep Learning: Survey & Future Directionscited by 5
  22. 2024Low Latency Transformer Inference on FPGAs for Physics Applications with hls4mlcited by 5
  23. 2025Neural Architecture Codesign for Fast Physics Applicationscited by 5
  24. 2025MetaML-Pro: Cross-Stage Design Flow Automation for Efficient Deep Learning Accelerationcited by 4
  25. 2020ESP4ML: Platform-Based Design of Systems-on-Chip for Embedded Machine Learningcited by 3
  26. 2023MetaML: Automating Customizable Cross-Stage Design-Flow for Deep Learning Accelerationcited by 3
  27. 2023ML-based Real-Time Control at the Edge: An Approach Using hls4mlcited by 3
  28. 2023ACCL+: an FPGA-Based Collective Engine for Distributed Applicationscited by 3
  29. 2024rule4ml: An Open-Source Tool for Resource Utilization and Latency Estimation for ML Models on FPGAcited by 3
  30. 2023A Dataflow Compiler for Efficient LLM Inference using Custom Microscaling Formatscited by 2
  31. 2025Comparative Analysis of FPGA and GPU Performance for Machine Learning-Based Track Reconstruction at LHCbcited by 1
  32. 2025Real-Time Cell Sorting with Scalable In Situ FPGA-Accelerated Deep Learningcited by 1
  33. 2025Coyote v2: Raising the Level of Abstraction for Data Center FPGAscited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 7.

8 papers.

  1. 2025AIE4ML: An End-to-End Framework for Compiling Neural Networks for the Next Generation of AMD AI Enginescited by 5
  2. 2026Interpreting "Interpretability" and Explaining "Explainability" in Machine Learning in Physicscited by 2
  3. 2026Enabling Low-Latency Machine learning on Radiation-Hard FPGAs with hls4mlcited by 1
  4. 2026Spiking Neural Network inference on FPGAs with hls4mlcited by 1
  5. 2026Taming the Exponential: A Fast Softmax Surrogate for Integer-Native Edge Inferencecited by 0
  6. 2026Reconfigurable Computing Challenge: Real-Time Graph Neural Networks for Online Event Selection in Big Sciencecited by 0
  7. 2026SNAC-Pack 2.0: Scaled-Out Surrogate Neural Architecture Codesigncited by 0
  8. 2026Machine Learning-Based Reconstruction for Resistive Silicon Sensorscited by 0