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- Welcome to hls4ml’s documentation! — hls4ml 1. 3. 0 documentation
Welcome to hls4ml’s documentation! hls4ml is a Python package for machine learning inference in FPGAs We create firmware implementations of machine learning algorithms using high level synthesis language (HLS) We translate traditional open-source machine learning package models into HLS that can be configured for your use-case! The project is currently in development, so please let us know
- Introduction — hls4ml 1. 3. 0 documentation
The hls4ml tool saves the time investment needed to convert a neural network to a hardware design language or even HLS code, thus allowing for rapid prototyping For more detailed information on technical details of hls4ml, read the “Internals” section of our documentation or our References page
- Setup and Quick Start — hls4ml 1. 3. 0 documentation
The hls4ml library requires python 3 10 or later, and depends on a number of Python packages and external tools for synthesis and simulation Python dependencies are automatically managed by pip or conda
- Concepts · GitBook
Concepts [Under Construction, for more detailed information see References] The goal of hls4ml is to provide an efficient and fast translation of machine learning models The resulting HLS project can be then used to produce an IP which can be plugged into more complex designs or be used to create a kernal for CPU co-processing The workflow for hls4ml is illustrated below
- Frequently asked questions — hls4ml 1. 3. 0 documentation
Frequently asked questions What is hls4ml? hls4ml is a tool for converting neural network models into FPGA firmware hls4ml is aimed at low-latency applications, such as triggering at the Large Hadron Collider (LHC) at CERN, but is applicable to other domains requiring microsecond latency See the full documentation for more details How does hls4ml work? hls4ml takes the models from Keras
- Concepts — hls4ml 1. 3. 0 documentation
With hls4ml, each layer of output values is calculated independently in sequence, using pipelining to speed up the process by accepting new inputs after an initiation interval The activations, if nontrivial, are precomputed To ensure optimal performance, the user can control aspects of their model, principally: Size Compression - Though not explicitly part of the hls4ml package, this is an
- Quick Start · GitBook
For further information about how to use hls4ml, do: hls4ml --help or hls4ml -h If you need help for a particular command, hls4ml command -h will show help for the requested command
- Status and Features — hls4ml 0. 8. 1 documentation
Other feature notes: hls4ml is tested on Linux, and supports Vivado HLS versions 2018 2 to 2020 1 Intel HLS versions 20 1 to 21 4 Vitis HLS versions 2022 2 to 2024 1 Windows and macOS are not supported BDT support has moved to the Conifer package Example Models We also provide and document several example hls4ml models in this GitHub repository, which is included as a submodule You can check
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