fastmachinelearning/hls4ml
Nov 2024 – Mar 2026
15 Months active
Languages Used
C++PythonRSTYAMLrstShellTOMLMarkdown
Technical Skills
Code FormattingCode RefactoringCode Style EnforcementConfiguration ManagementDebuggingDeep Learning Frameworks
Jan-Frederik Schulte
PROFILE
Jan-frederik Schulte
Jonas Schulte contributed to the fastmachinelearning/hls4ml repository by engineering robust model conversion pipelines and backend integrations for deep learning on FPGAs. He developed and refined PyTorch-to-HLS conversion, implemented support for advanced layers like einsum and PReLU, and improved multi-output and RNN handling. Using Python, C++, and PyTorch, Jonas focused on code modularity, test automation, and CI/CD reliability, introducing standardized formatting and modernizing workflows with GitHub Actions. His work emphasized maintainability through documentation, code refactoring, and configuration management, resulting in a more reliable, extensible codebase that supports diverse model architectures and streamlines deployment across hardware backends.
Overall Statistics
Feature vs Bugs
70%Features
Repository Contributions
61Total
Bugs
9
Commits
61
Features
21
Lines of code
4,213
Activity Months15
Your Network
345 peopleSame Organization
@cern.ch326
Shared Repositories
19
Work History
March 2026
4 Commits • 3 Features
Mar 1, 2026March 2026 performance summary for fastmachinelearning/hls4ml focused on reliability, documentation, and transparency. Key outcomes include CI/CD artifact management improvements, a formal release with updated citations, and explicit OneAPI feature status documentation. Major bugs fixed: none reported this month. Overall impact: improved artifact reproducibility in CI, clearer licensing and citation guidance, and reduced support ambiguity for OneAPI features. Technologies demonstrated: GitHub Actions (CI/CD workflows), semantic versioning and release processes, CITATION.cff maintenance, and OneAPI feature documentation.
4 Commits • 3 Features
Mar 1, 2026March 2026 performance summary for fastmachinelearning/hls4ml focused on reliability, documentation, and transparency. Key outcomes include CI/CD artifact management improvements, a formal release with updated citations, and explicit OneAPI feature status documentation. Major bugs fixed: none reported this month. Overall impact: improved artifact reproducibility in CI, clearer licensing and citation guidance, and reduced support ambiguity for OneAPI features. Technologies demonstrated: GitHub Actions (CI/CD workflows), semantic versioning and release processes, CITATION.cff maintenance, and OneAPI feature documentation.
March 2026
January 2026
4 Commits • 1 Features
Jan 1, 2026January 2026 (Month: 2026-01) focused on modernizing the test suite for fastmachinelearning/hls4ml to ensure Pytest 9 compatibility, improve readability, and stabilize HLS model test identifiers. The work consolidates test improvements, removes legacy parametrized fixtures, and tightens test output elucidation, setting a stable foundation for CI and downstream contributors.
January 2026
4 Commits • 1 Features
Jan 1, 2026January 2026 (Month: 2026-01) focused on modernizing the test suite for fastmachinelearning/hls4ml to ensure Pytest 9 compatibility, improve readability, and stabilize HLS model test identifiers. The work consolidates test improvements, removes legacy parametrized fixtures, and tightens test output elucidation, setting a stable foundation for CI and downstream contributors.
December 2025
2 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary for fastmachinelearning/hls4ml — focused on CI/CD modernization and artifact handling in GitHub Actions, delivering stability and efficiency improvements across the build pipeline.
2 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary for fastmachinelearning/hls4ml — focused on CI/CD modernization and artifact handling in GitHub Actions, delivering stability and efficiency improvements across the build pipeline.
December 2025
November 2025
1 Commits • 1 Features
Nov 1, 2025November 2025 monthly summary for fastmachinelearning/hls4ml focused on code quality improvements and standardization. Delivered Ruff-based formatting and a version upgrade to ensure a consistent, maintainable codebase with reduced review overhead. The changes lay the groundwork for faster onboarding and fewer formatting-related defects.
November 2025
1 Commits • 1 Features
Nov 1, 2025November 2025 monthly summary for fastmachinelearning/hls4ml focused on code quality improvements and standardization. Delivered Ruff-based formatting and a version upgrade to ensure a consistent, maintainable codebase with reduced review overhead. The changes lay the groundwork for faster onboarding and fewer formatting-related defects.
October 2025
1 Commits • 1 Features
Oct 1, 2025October 2025 monthly summary for fastmachinelearning/hls4ml focused on strengthening CI/CD security and reliability. Primary work delivered a security-focused upgrade to GitHub Actions workflows, upgrading to actions/checkout@v5 and implementing granular token handling and permissions to reduce risk and improve automation quality. No major bugs fixed this month; the effort centered on governance, security, and maintainability of the CI/CD pipelines.
1 Commits • 1 Features
Oct 1, 2025October 2025 monthly summary for fastmachinelearning/hls4ml focused on strengthening CI/CD security and reliability. Primary work delivered a security-focused upgrade to GitHub Actions workflows, upgrading to actions/checkout@v5 and implementing granular token handling and permissions to reduce risk and improve automation quality. No major bugs fixed this month; the effort centered on governance, security, and maintainability of the CI/CD pipelines.
October 2025
September 2025
2 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for fastmachinelearning/hls4ml: Key bug fix improving PyTorch model conversion for RNN/LSTM/GRU channels-last, test stability, and targeted documentation maintenance for multimodelgraph. Impact: higher reliability of the conversion pipeline, fewer CI failures, and clearer docs for users and contributors. Tech stack: PyTorch, channels-last data format, pytest, Python; documentation tooling.
September 2025
2 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for fastmachinelearning/hls4ml: Key bug fix improving PyTorch model conversion for RNN/LSTM/GRU channels-last, test stability, and targeted documentation maintenance for multimodelgraph. Impact: higher reliability of the conversion pipeline, fewer CI failures, and clearer docs for users and contributors. Tech stack: PyTorch, channels-last data format, pytest, Python; documentation tooling.
August 2025
3 Commits
Aug 1, 2025August 2025 (2025-08) monthly summary for fastmachinelearning/hls4ml focused on stability, correctness, and compatibility across backends. Implemented three high-impact fixes: Time Distributed Parser return propagation, OneAPI type validation to drop ac_float usage, and Vitis HLS mode flag inclusion. These improvements reduce runtime errors, improve cross-backend reliability, and streamline deployment for 2023.1 toolchains.
3 Commits
Aug 1, 2025August 2025 (2025-08) monthly summary for fastmachinelearning/hls4ml focused on stability, correctness, and compatibility across backends. Implemented three high-impact fixes: Time Distributed Parser return propagation, OneAPI type validation to drop ac_float usage, and Vitis HLS mode flag inclusion. These improvements reduce runtime errors, improve cross-backend reliability, and streamline deployment for 2023.1 toolchains.
August 2025
July 2025
1 Commits • 1 Features
Jul 1, 2025July 2025 (2025-07) monthly summary for fastmachinelearning/hls4ml: Implemented PReLU activation support in the infer_precision pass, enhanced tests for activation correctness, and added safeguards to prevent unsupported PReLU configurations. This work improves model compatibility and inference reliability, while strengthening test coverage and maintainability across the activation pipeline.
July 2025
1 Commits • 1 Features
Jul 1, 2025July 2025 (2025-07) monthly summary for fastmachinelearning/hls4ml: Implemented PReLU activation support in the infer_precision pass, enhanced tests for activation correctness, and added safeguards to prevent unsupported PReLU configurations. This work improves model compatibility and inference reliability, while strengthening test coverage and maintainability across the activation pipeline.
June 2025
2 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for fastmachinelearning/hls4ml: Focused on stability, parser enhancements, and expanded model coverage for hardware acceleration. Implemented a fix to remove test flakiness in dense unrolled RNN tests by rounding inputs to fixed-point, and extended the PyTorch parser to support einsum operations, including equation extraction and output shape inference, paving the way for converting einsum-heavy models to HLS. Additionally, expanded test coverage for einsum-related paths (outer product, batch matmul) to ensure robustness. Overall impact: more reliable CI, higher confidence in model conversion, and a broader set of deployable architectures. Technologies: Python, fixed-point arithmetic, PyTorch integration, automatic testing, HLS/FPGA conversion workflows.
2 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for fastmachinelearning/hls4ml: Focused on stability, parser enhancements, and expanded model coverage for hardware acceleration. Implemented a fix to remove test flakiness in dense unrolled RNN tests by rounding inputs to fixed-point, and extended the PyTorch parser to support einsum operations, including equation extraction and output shape inference, paving the way for converting einsum-heavy models to HLS. Additionally, expanded test coverage for einsum-related paths (outer product, batch matmul) to ensure robustness. Overall impact: more reliable CI, higher confidence in model conversion, and a broader set of deployable architectures. Technologies: Python, fixed-point arithmetic, PyTorch integration, automatic testing, HLS/FPGA conversion workflows.
June 2025
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary for fastmachinelearning/hls4ml: Focused on refining PyTorch extension API integration. Delivered a naming refactor and API support clarification to reduce conflicts and improve cross-framework compatibility. Updated docs and optimizer registration to reflect the new HReverseTorch naming. No major bugs reported; tests strengthened and cross-framework readiness improved. This work enhances maintainability, reduces integration risk, and positions the project for smoother collaboration with Keras and PyTorch extension APIs.
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary for fastmachinelearning/hls4ml: Focused on refining PyTorch extension API integration. Delivered a naming refactor and API support clarification to reduce conflicts and improve cross-framework compatibility. Updated docs and optimizer registration to reflect the new HReverseTorch naming. No major bugs reported; tests strengthened and cross-framework readiness improved. This work enhances maintainability, reduces integration risk, and positions the project for smoother collaboration with Keras and PyTorch extension APIs.
March 2025
5 Commits • 2 Features
Mar 1, 2025March 2025 monthly summary for fastmachinelearning/hls4ml: Focused on improving onboarding experience, documentation quality, and dependency management to enable lighter builds and faster iteration. Delivered clear v1.1.0 documentation, clearer API notes, and a refactored codebase to decouple PyTorch dependency. No explicit bug fixes documented this month; stability gains come from modularization and updated release notes.
5 Commits • 2 Features
Mar 1, 2025March 2025 monthly summary for fastmachinelearning/hls4ml: Focused on improving onboarding experience, documentation quality, and dependency management to enable lighter builds and faster iteration. Delivered clear v1.1.0 documentation, clearer API notes, and a refactored codebase to decouple PyTorch dependency. No explicit bug fixes documented this month; stability gains come from modularization and updated release notes.
March 2025
February 2025
9 Commits • 2 Features
Feb 1, 2025February 2025 monthly summary for fastmachinelearning/hls4ml. Focused on expanding PyTorch model conversion capabilities, strengthening graph integrity, and hardening the build/dependency pipelines to improve reliability across backends. Business value delivered includes enabling multi-output architectures, reliable cross-backend conversions, and more robust deployment pipelines.
February 2025
9 Commits • 2 Features
Feb 1, 2025February 2025 monthly summary for fastmachinelearning/hls4ml. Focused on expanding PyTorch model conversion capabilities, strengthening graph integrity, and hardening the build/dependency pipelines to improve reliability across backends. Business value delivered includes enabling multi-output architectures, reliable cross-backend conversions, and more robust deployment pipelines.
January 2025
3 Commits • 1 Features
Jan 1, 2025January 2025: Delivered critical transpose handling improvements in ChannelsLastConverter for fastmachinelearning/hls4ml, with enhanced 3D transpose error handling for IO streams and a default configuration change (transpose_outputs) to False. Updated tests to reflect new defaults and aligned pytest configuration. This work reduces runtime errors on 3D data, stabilizes model deployment pipelines, and improves inference reliability for real-time and batch workloads.
3 Commits • 1 Features
Jan 1, 2025January 2025: Delivered critical transpose handling improvements in ChannelsLastConverter for fastmachinelearning/hls4ml, with enhanced 3D transpose error handling for IO streams and a default configuration change (transpose_outputs) to False. Updated tests to reflect new defaults and aligned pytest configuration. This work reduces runtime errors on 3D data, stabilizes model deployment pipelines, and improves inference reliability for real-time and batch workloads.
January 2025
December 2024
15 Commits • 2 Features
Dec 1, 2024December 2024 monthly summary for fastmachinelearning/hls4ml highlights substantial progress in PyTorch integration, improved converter robustness, and more reliable CI/testing. Delivered concrete features and fixes that enhance PyTorch model workflows, reduce risk in model parsing, and accelerate feedback cycles, contributing to faster time-to-market and more dependable deployments for end users.
December 2024
15 Commits • 2 Features
Dec 1, 2024December 2024 monthly summary for fastmachinelearning/hls4ml highlights substantial progress in PyTorch integration, improved converter robustness, and more reliable CI/testing. Delivered concrete features and fixes that enhance PyTorch model workflows, reduce risk in model parsing, and accelerate feedback cycles, contributing to faster time-to-market and more dependable deployments for end users.
November 2024
8 Commits • 3 Features
Nov 1, 2024Nov 2024 monthly summary for fastmachinelearning/hls4ml: Delivered core PyTorch-to-HLS converter improvements, expanded default configurations for smoother model conversion, and reinforced code quality and tooling to boost maintainability and reliability. Focused on business value: more accurate conversions, faster onboarding for new models, reduced troubleshooting, and a more robust development workflow. Technologies include Python, PyTorch, QONNX, BRAM tuning, pre-commit tooling, and clean-code practices.
8 Commits • 3 Features
Nov 1, 2024Nov 2024 monthly summary for fastmachinelearning/hls4ml: Delivered core PyTorch-to-HLS converter improvements, expanded default configurations for smoother model conversion, and reinforced code quality and tooling to boost maintainability and reliability. Focused on business value: more accurate conversions, faster onboarding for new models, reduced troubleshooting, and a more robust development workflow. Technologies include Python, PyTorch, QONNX, BRAM tuning, pre-commit tooling, and clean-code practices.
November 2024
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Quality Metrics
Correctness92.8%
Maintainability92.4%
Architecture91.0%
Performance85.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++MarkdownPythonRSTShellTOMLYAMLreStructuredTextrst
Technical Skills
API DevelopmentBackend DevelopmentBuild SystemsC++ Template MetaprogrammingCI/CDCode ConversionCode FormattingCode OrganizationCode RefactoringCode Style EnforcementCompiler DesignConfiguration ManagementContinuous IntegrationDebuggingDeep Learning
Repositories Contributed To
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