fastmachinelearning/hls4ml
Jun 2025 – Jun 2025
1 Month active
Languages Used
C++PythonTclVerilog
Technical Skills
FPGA DevelopmentHLSHardware DesignIP StitchingModel SplittingParallel Compilation
dimdano
PROFILE
Dimdano
In June 2025, Dimitrios Danopoulos developed core multi-graph architecture for the fastmachinelearning/hls4ml repository, enabling partitioning of a single model into multiple subgraphs for parallel FPGA builds. He implemented new management classes and backend enhancements in C++ and Python to support this parallelism, along with a Vivado-based stitching flow that integrates and simulates the resulting hardware designs. His work included automated testbench generation for stitched models, streamlining end-to-end validation. By focusing on model splitting, IP stitching, and RTL simulation, Dimitrios delivered a robust foundation for scalable, faster hardware deployment in machine learning accelerator workflows using HLS and Vivado.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
2,469
Activity Months1
Your Network
325 people
Work History
June 2025
1 Commits • 1 Features
Jun 1, 2025In June 2025, delivered core multi-graph architecture for hls4ml enabling a single model to be partitioned into subgraphs, allowing parallel subgraph builds and a Vivado stitching flow to generate and simulate integrated hardware designs. Implemented new multi-graph management classes and backend enhancements to support parallel builds and stitching, plus automated testbench generation for stitched designs. This work lays the groundwork for scalable hardware deployment and faster iteration cycles across FPGA-inclusive ML accelerators.
1 Commits • 1 Features
Jun 1, 2025In June 2025, delivered core multi-graph architecture for hls4ml enabling a single model to be partitioned into subgraphs, allowing parallel subgraph builds and a Vivado stitching flow to generate and simulate integrated hardware designs. Implemented new multi-graph management classes and backend enhancements to support parallel builds and stitching, plus automated testbench generation for stitched designs. This work lays the groundwork for scalable hardware deployment and faster iteration cycles across FPGA-inclusive ML accelerators.
June 2025
Activity
Loading activity data...
Quality Metrics
Correctness90.0%
Maintainability90.0%
Architecture100.0%
Performance90.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++PythonTclVerilog
Technical Skills
FPGA DevelopmentHLSHardware DesignIP StitchingModel SplittingParallel CompilationRTL SimulationTestbench GenerationVivado
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline