hls4ml and hls4ml-tutorial
These are ecosystem siblings where the tutorial repository provides instructional notebooks and examples that teach users how to apply the core hls4ml framework for converting neural networks to synthesizable HLS code on FPGAs.
About hls4ml
fastmachinelearning/hls4ml
Machine learning on FPGAs using HLS
Automatically converts trained models from Keras, PyTorch, and other frameworks into synthesizable HLS code, supporting multiple vendor backends (Xilinx Vivado/Vitis, Intel, Catapult). Optimizes for sub-microsecond latency inference through techniques like quantization to binary/ternary precision, distributed arithmetic, and CNN/semantic segmentation acceleration. Originally developed for high-energy physics trigger systems but now deployed across quantum control, satellite monitoring, and biomedical signal processing applications.
About hls4ml-tutorial
fastmachinelearning/hls4ml-tutorial
Tutorial notebooks for hls4ml
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