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hardware-aware-ml

Here are 4 public repositories matching this topic...

christopher-altman / sqnt-hardware-demo

Adaptive quantum networks in practice: superposed graph topologies and operator-space spatialization, with reproducible hardware-relevant demos and figures.

Updated Jan 19, 2026
Python

or4k2l / robust-vision

Production-ready framework for training robust computer vision models. Features multi-GPU support, EMA tracking, label smoothing, and comprehensive robustness evaluation across 4 noise types. Includes scalable TF.Data pipeline, automated testing, Docker support, and CLI tools. Install: pip install robust-vision

python docker machine-learning computer-vision deep-learning pattern-recognition autonomous-driving kitti-dataset multi-gpu neuromorphic-hardware neuromorphic-computing memristive-crossbar jax confidence-estimation production-ml noise-robustness implicit-regularization robust-ml hardware-aware-ml

Updated Mar 13, 2026
Python

yoctocandela / silkworm-fpga-ml

Hardware-aware classical ML for silkworm Grasserie disease detection on PYNQ-Z2 — MYZ307E term project, ITU 2026

fpga random-forest itu term-project local-binary-patterns pynq-z2 zynq-7020 edge-ml classical-machine-learning agricultural-ai hardware-aware-ml silkworm-disease-detection sericulture recall-optimized-training

Updated May 15, 2026
Python

Aayush-Gangwar / hardware-aware-deep-learning-inference-on-fpga

Hardware-aware deep learning inference framework using C++, Vitis HLS, and FPGA-oriented RTL generation for CNN, MLP, and VGG16 models.

deep-learning hls cnn rtl xilinx vivado computer-architecture quantization pynq hardware-acceleration fpga-programming embedded-ai edge-ai vitis-hls hardware-aware-ml

Updated May 10, 2026
C++

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