fastmachinelearning/hls4ml
Oct 2024 – Sep 2025
11 Months active
Languages Used
C++PythonRSTrstreStructuredTextCMake
Technical Skills
Backend DevelopmentConvolutional Neural NetworksDeep LearningFPGA DevelopmentHLSKeras
Jovan Mitrevski
PROFILE
Jovan Mitrevski
Over 11 months, Jovan Mitrevski contributed to fastmachinelearning/hls4ml by developing and refining backend infrastructure for FPGA and oneAPI model compilation, focusing on robust model conversion and quantization workflows. He implemented features such as generalized transpose operations, BipolarQuant binary quantization, and fixed-point precision testing, using C++, Python, and SYCL. His work addressed edge cases in ONNX and PyTorch model handling, improved documentation, and enhanced test coverage to ensure reliability across hardware targets. By integrating configuration management and streamlining build processes, Jovan delivered stable, production-ready pipelines that reduced deployment risk and improved maintainability for machine learning model acceleration.
Overall Statistics
Feature vs Bugs
70%Features
Repository Contributions
35Total
Bugs
7
Commits
35
Features
16
Lines of code
12,546
Activity Months11
Your Network
59 people
Work History
September 2025
2 Commits • 2 Features
Sep 1, 20252025-09 monthly summary for fastmachinelearning/hls4ml: Delivered key quantization and emulation enhancements to support production-ready deployment across multiple backends. Implemented BipolarQuant binary quantization in QONNX with new optimizer passes and non-unit scaling handling; updated backends (Catapult, Vitis, oneAPI) and added tests. Added CMSSW emulation support for the Vitis backend with write_emulation_constants, updated templates, and a new pytest test_emulator. No explicit bug-fix commits recorded this month; overall the month advanced hardware-friendly inference pipelines, with broader test coverage and cross-backend compatibility.
2 Commits • 2 Features
Sep 1, 20252025-09 monthly summary for fastmachinelearning/hls4ml: Delivered key quantization and emulation enhancements to support production-ready deployment across multiple backends. Implemented BipolarQuant binary quantization in QONNX with new optimizer passes and non-unit scaling handling; updated backends (Catapult, Vitis, oneAPI) and added tests. Added CMSSW emulation support for the Vitis backend with write_emulation_constants, updated templates, and a new pytest test_emulator. No explicit bug-fix commits recorded this month; overall the month advanced hardware-friendly inference pipelines, with broader test coverage and cross-backend compatibility.
September 2025
August 2025
1 Commits • 1 Features
Aug 1, 2025August 2025 focused on stabilizing PyTorch model conversion in hls4ml by delivering reliable stream cloning and reshape handling, addressing data transmission errors with oneAPI, and refining reshape interpretation to boost prediction accuracy. These improvements reduce end-to-end model conversion risk and improve production reliability.
August 2025
1 Commits • 1 Features
Aug 1, 2025August 2025 focused on stabilizing PyTorch model conversion in hls4ml by delivering reliable stream cloning and reshape handling, addressing data transmission errors with oneAPI, and refining reshape interpretation to boost prediction accuracy. These improvements reduce end-to-end model conversion risk and improve production reliability.
July 2025
3 Commits • 2 Features
Jul 1, 2025July 2025 monthly summary for fastmachinelearning/hls4ml: Key improvements centered on testing realism and UX cleanliness. Implemented robust testing configuration and realistic Keras model initialization to improve test coverage for model conversion/compilation. Removed extraneous debug outputs and warnings to deliver a cleaner user experience in the oneAPI report and FPGABackend. These changes strengthen CI reliability, reduce noise in logs, and speed up debugging and iteration.
3 Commits • 2 Features
Jul 1, 2025July 2025 monthly summary for fastmachinelearning/hls4ml: Key improvements centered on testing realism and UX cleanliness. Implemented robust testing configuration and realistic Keras model initialization to improve test coverage for model conversion/compilation. Removed extraneous debug outputs and warnings to deliver a cleaner user experience in the oneAPI report and FPGABackend. These changes strengthen CI reliability, reduce noise in logs, and speed up debugging and iteration.
July 2025
June 2025
1 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for fastmachinelearning/hls4ml focusing on OneAPI FPGA backend defaults, build flag integration, and improved configuration stability. The changes reduce variability due to aggressive optimi zation, improve reproducibility, and align defaults with supported hardware configurations.
June 2025
1 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for fastmachinelearning/hls4ml focusing on OneAPI FPGA backend defaults, build flag integration, and improved configuration stability. The changes reduce variability due to aggressive optimi zation, improve reproducibility, and align defaults with supported hardware configurations.
April 2025
2 Commits
Apr 1, 2025Concise monthly summary for 2025-04 focusing on business value and technical achievements in fastmachinelearning/hls4ml. The month centered on stabilizing backend behavior and improving model transformation accuracy by fixing critical edge-case issues; added regression coverage for ONNX multi-output handling to prevent future regressions and to ensure reliable model deployment across backends.
2 Commits
Apr 1, 2025Concise monthly summary for 2025-04 focusing on business value and technical achievements in fastmachinelearning/hls4ml. The month centered on stabilizing backend behavior and improving model transformation accuracy by fixing critical edge-case issues; added regression coverage for ONNX multi-output handling to prevent future regressions and to ensure reliable model deployment across backends.
April 2025
March 2025
2 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for fastmachinelearning/hls4ml. Focused on test determinism and hardware-aware validation. Delivered fixed-point precision testing enhancements for recurrent PyTorch models, introducing fixed-point input representation and updating config_from_pytorch_model with default_precision='fixed<32,16>'. No major bugs fixed this month; the work primarily increases reliability and hardware-readiness of the test suite. Overall impact: more stable tests, smoother hardware validation, and clearer signals for performance and deployment improvements. Technologies/skills: fixed-point arithmetic, ap_fixed<32,16>, PyTorch integration, pytest configuration, hardware-aware testing.
March 2025
2 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for fastmachinelearning/hls4ml. Focused on test determinism and hardware-aware validation. Delivered fixed-point precision testing enhancements for recurrent PyTorch models, introducing fixed-point input representation and updating config_from_pytorch_model with default_precision='fixed<32,16>'. No major bugs fixed this month; the work primarily increases reliability and hardware-readiness of the test suite. Overall impact: more stable tests, smoother hardware validation, and clearer signals for performance and deployment improvements. Technologies/skills: fixed-point arithmetic, ap_fixed<32,16>, PyTorch integration, pytest configuration, hardware-aware testing.
February 2025
3 Commits • 2 Features
Feb 1, 2025February 2025 monthly performance summary for fastmachinelearning/hls4ml. Delivered backend enhancements and a critical correctness fix, focusing on business value and technical robustness. Highlights include generalized transpose support for the oneAPI backend, configurable tarball generation to streamline builds, and a targeted fix to attribute propagation in move_scales.py, reducing warnings and ensuring correct scaling behavior across backends.
3 Commits • 2 Features
Feb 1, 2025February 2025 monthly performance summary for fastmachinelearning/hls4ml. Delivered backend enhancements and a critical correctness fix, focusing on business value and technical robustness. Highlights include generalized transpose support for the oneAPI backend, configurable tarball generation to streamline builds, and a targeted fix to attribute propagation in move_scales.py, reducing warnings and ensuring correct scaling behavior across backends.
February 2025
January 2025
1 Commits
Jan 1, 2025January 2025 monthly summary for fastmachinelearning/hls4ml focused on robustness improvements in graph construction. No new user-facing features delivered this month; a critical bug fix was implemented to prevent crashes when inserting nodes with no inputs, improving stability for edge cases and downstream workflows.
January 2025
1 Commits
Jan 1, 2025January 2025 monthly summary for fastmachinelearning/hls4ml focused on robustness improvements in graph construction. No new user-facing features delivered this month; a critical bug fix was implemented to prevent crashes when inserting nodes with no inputs, improving stability for edge cases and downstream workflows.
December 2024
9 Commits • 4 Features
Dec 1, 2024Month: 2024-12. This period delivered notable reliability improvements and alignment efforts for fastmachinelearning/hls4ml, focusing on guiding users toward compatible configurations, stabilizing optimization pipelines, and keeping dependencies current. Business value was enhanced through reduced misconfiguration risk in quantization, more robust deployment-ready optimization passes, and clearer documentation for backend configurations and precision inference.
9 Commits • 4 Features
Dec 1, 2024Month: 2024-12. This period delivered notable reliability improvements and alignment efforts for fastmachinelearning/hls4ml, focusing on guiding users toward compatible configurations, stabilizing optimization pipelines, and keeping dependencies current. Business value was enhanced through reduced misconfiguration risk in quantization, more robust deployment-ready optimization passes, and clearer documentation for backend configurations and precision inference.
December 2024
November 2024
8 Commits • 2 Features
Nov 1, 2024November 2024: Delivered key usability and reliability improvements for fastmachinelearning/hls4ml. Focused on documentation quality, precision-control capabilities in model conversion, and solid graph/optimizer correctness, enabling safer deployments and faster iteration.
November 2024
8 Commits • 2 Features
Nov 1, 2024November 2024: Delivered key usability and reliability improvements for fastmachinelearning/hls4ml. Focused on documentation quality, precision-control capabilities in model conversion, and solid graph/optimizer correctness, enabling safer deployments and faster iteration.
October 2024
3 Commits • 1 Features
Oct 1, 2024October 2024 highlights for fastmachinelearning/hls4ml: delivered foundational OneAPI backend for HLS4ML, expanded hardware target options, and fixed critical parsing and indexing issues to improve robustness and reliability of the HLS code generation workflow; increased test coverage to guard against regressions. Business value focus: broadened deployment options, improved accuracy of model-to-HLS translations, and strengthened maintainability of the codebase.
3 Commits • 1 Features
Oct 1, 2024October 2024 highlights for fastmachinelearning/hls4ml: delivered foundational OneAPI backend for HLS4ML, expanded hardware target options, and fixed critical parsing and indexing issues to improve robustness and reliability of the HLS code generation workflow; increased test coverage to guard against regressions. Business value focus: broadened deployment options, improved accuracy of model-to-HLS translations, and strengthened maintainability of the codebase.
October 2024
Activity
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Quality Metrics
Correctness89.2%
Maintainability90.8%
Architecture86.8%
Performance80.6%
AI Usage20.6%
Skills & Technologies
Programming Languages
C++CMakePythonRSTreStructuredTextrst
Technical Skills
Backend DevelopmentBackend IntegrationC++Code CleanupCompiler DevelopmentConfiguration ManagementConvolutional Neural NetworksDebuggingDeep LearningDeep Learning FrameworksDocumentationDocumentation ManagementFPGA DevelopmentFPGA Toolchain IntegrationFixed-Point Arithmetic
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