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
This project helps domain experts in fields like high-energy physics, quantum computing, or aerospace who need to process real-time data with extremely low latency. It takes machine learning models built with common frameworks and converts them into specialized firmware for FPGAs. The output is a highly optimized hardware implementation of your model, enabling rapid decision-making directly on hardware.
About hls4ml-tutorial
fastmachinelearning/hls4ml-tutorial
Tutorial notebooks for hls4ml
This project provides step-by-step guides to help machine learning engineers optimize their trained deep learning models for deployment on specialized hardware like FPGAs. It takes a pre-trained model and converts it into a highly efficient hardware implementation, outputting an optimized design ready for synthesis. This is for engineers looking to accelerate their machine learning inferences in applications requiring low latency and high throughput.
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