fastmachinelearning/hls4ml
May 2025 – Jun 2026
2 Months active
Languages Used
C++Python
Technical Skills
Backend DevelopmentFPGA DevelopmentHigh-Level Synthesis (HLS)Model OptimizationTestingKeras
makoeppel
PROFILE
Makoeppel
Over a two-month period, contributed to the fastmachinelearning/hls4ml repository by delivering two major features focused on FPGA and deep learning model deployment. Extended clone streaming capabilities to support up to seven outputs across Catapult, OneAPI, Quartus, and Vivado backends, updating both Python and C++ components to improve multi-output neural network synthesis. Later, implemented QKeras v3 compatibility, adding new layer handlers, comprehensive tests, and documentation to streamline deployment of quantized models. Emphasized maintainability by refining code structure and test coverage. Work demonstrated strong backend development, high-level synthesis, and Python expertise, with a focus on robust, cross-backend functionality and future-proof integration.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
1,206
Activity Months2
Your Network
22 peopleSame Organization
@phys.ethz.ch1
Shared Repositories
21
Work History
June 2026
1 Commits • 1 Features
Jun 1, 2026June 2026 monthly summary: Focused on extending hls4ml with QKeras v3 compatibility, enabling users to deploy QKeras-based models on FPGA targets with minimal changes. Delivered new QKeras v3 support including layer handlers, tests, and documentation, and updated repository configuration to enable qkerasv2 and qkerasv3. Strengthened test reliability with targeted fixes, introduced new testing coverage for QKeras v3, and added maintainability improvements across the codebase.
1 Commits • 1 Features
Jun 1, 2026June 2026 monthly summary: Focused on extending hls4ml with QKeras v3 compatibility, enabling users to deploy QKeras-based models on FPGA targets with minimal changes. Delivered new QKeras v3 support including layer handlers, tests, and documentation, and updated repository configuration to enable qkerasv2 and qkerasv3. Strengthened test reliability with targeted fixes, introduced new testing coverage for QKeras v3, and added maintainability improvements across the codebase.
June 2026
May 2025
1 Commits • 1 Features
May 1, 2025May 2025: Delivered an extension to clone streaming in hls4ml to support up to 7 outputs across Catapult, OneAPI, Quartus, and Vivado backends. The work increased the maximum clone count in the clone.py pass, added new clone_stream overloads in the backend C++ template headers, and included regression tests validating the extended functionality. This enhancement improves model cloning scalability in FPGA synthesis workflows, enabling more flexible multi-output deployments with consistent behavior across backends, reducing manual rework and accelerating time-to-market for multi-output neural network models.
May 2025
1 Commits • 1 Features
May 1, 2025May 2025: Delivered an extension to clone streaming in hls4ml to support up to 7 outputs across Catapult, OneAPI, Quartus, and Vivado backends. The work increased the maximum clone count in the clone.py pass, added new clone_stream overloads in the backend C++ template headers, and included regression tests validating the extended functionality. This enhancement improves model cloning scalability in FPGA synthesis workflows, enabling more flexible multi-output deployments with consistent behavior across backends, reducing manual rework and accelerating time-to-market for multi-output neural network models.
Activity
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Quality Metrics
Correctness90.0%
Maintainability90.0%
Architecture90.0%
Performance80.0%
AI Usage30.0%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
Backend DevelopmentFPGA DevelopmentHigh-Level Synthesis (HLS)KerasModel OptimizationPythonTestingdeep learningmachine learningquantization
Repositories Contributed To
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