fastmachinelearning/hls4ml
Nov 2024 – Feb 2026
3 Months active
Languages Used
C++Python
Technical Skills
HLS ImplementationModel OptimizationONNX ConversionPython DevelopmentTestingFixed-Point Arithmetic
Nicolò Ghielmetti
PROFILE
Nicolò Ghielmetti
Nicolo Ghielmetti contributed to the fastmachinelearning/hls4ml repository by developing features that enhance model compatibility, precision management, and test reliability. He implemented ONNX Resize node ingestion, enabling seamless integration and deployment of models using Resize, and introduced optimizer passes in Python to improve model compilation efficiency. Nicolo also delivered automatic type inference for parametrized activations, leveraging fixed-point arithmetic and type inference to streamline hardware deployment and reduce manual tuning. Additionally, he improved test output organization and CI artifact hygiene using pytest, ensuring reproducible and reliable testing. His work demonstrated depth in C++, Python, and machine learning optimization throughout the project.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
3
Lines of code
1,298
Activity Months3
Your Network
19 peopleShared Repositories
19
Work History
February 2026
1 Commits • 1 Features
Feb 1, 2026February 2026 monthly summary for fastmachinelearning/hls4ml focused on improving test reliability and artifact hygiene through test output organization, sanitization, and CI baseline management. These changes lay groundwork for faster iteration and safer refactors.
1 Commits • 1 Features
Feb 1, 2026February 2026 monthly summary for fastmachinelearning/hls4ml focused on improving test reliability and artifact hygiene through test output organization, sanitization, and CI baseline management. These changes lay groundwork for faster iteration and safer refactors.
February 2026
September 2025
1 Commits • 1 Features
Sep 1, 2025September 2025 — fastmachinelearning/hls4ml: Delivered automatic type inference for param_t in parametrized activations with constant precision inference, improved stability for precision handling, and strengthened test coverage and dependencies. The work focuses on enabling automatic precision management to support more efficient hardware deployments and reduce manual tuning efforts.
September 2025
1 Commits • 1 Features
Sep 1, 2025September 2025 — fastmachinelearning/hls4ml: Delivered automatic type inference for param_t in parametrized activations with constant precision inference, improved stability for precision handling, and strengthened test coverage and dependencies. The work focuses on enabling automatic precision management to support more efficient hardware deployments and reduce manual tuning efforts.
November 2024
1 Commits • 1 Features
Nov 1, 20242024-11 monthly summary for fastmachinelearning/hls4ml: Delivered ONNX/QONNX Resize node ingestion support enabling models using Resize to be ingested and compiled. Included tests with a tiny UNet model and a branched model, performed code cleanup, and added an optimizer pass to handle constant inputs for Resize, improving reliability and deployment readiness. This work extends ONNX compatibility, reduces model adaptation effort, and strengthens the platform's readiness for production workloads.
1 Commits • 1 Features
Nov 1, 20242024-11 monthly summary for fastmachinelearning/hls4ml: Delivered ONNX/QONNX Resize node ingestion support enabling models using Resize to be ingested and compiled. Included tests with a tiny UNet model and a branched model, performed code cleanup, and added an optimizer pass to handle constant inputs for Resize, improving reliability and deployment readiness. This work extends ONNX compatibility, reduces model adaptation effort, and strengthens the platform's readiness for production workloads.
November 2024
Activity
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Quality Metrics
Correctness93.4%
Maintainability86.6%
Architecture86.6%
Performance86.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
Fixed-Point ArithmeticHLS ImplementationMachine Learning OptimizationModel OptimizationONNX ConversionPythonPython DevelopmentTestingType InferenceUnit Testingpytestsoftware engineeringtesting
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