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Hls4ml Alternatives
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hlslib
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SaaSHub
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NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better hls4ml alternative or higher similarity.
hls4ml reviews and mentions
Posts with mentions or reviews of hls4ml.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-06-20.
- How to participate in open-source FPGA projects?
- Looking for HLS frameworks to start deploying DL algorithms on FPGAs
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Hi, What could be the best HLS tool for implementing neural networks on FPGA
I see that someone has already suggested hls4ml. I second that opinion. From my experience, it is extremely well documented. They have published papers which explain the scientific background. They have a really nice git page where they explain all the features of their tool. Additionally they also have an easy to follow tutorial of doing it from scratch using tensorflow networks. You can find all the information herehls4ml.
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5 layered CNN implementation on arduino/FPGAs [P]
Open source project that originated at Fermilab https://github.com/fastmachinelearning/hls4ml (based on Xilinx Vivado which has been replaced by Vitis)
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Help needed to build a Hardware accelerator for CNN's
You may check the hls4ml framework: it's a "translator" from the ML model (Keras, PyTorch) to a synthesizable High-Level Synthesis (HLS) IP Core.
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Sub ms - 3ms Latency Vision task on FPGA
It really depends on the type of data you are using. There may (or may not) be some trade offs and sacrifices. There are frameworks which can basically translate your neural network information from a high level python code into equivalent HLS code which is optimized for low latency when inferred on FPGAs. Some frameworks which might be useful for you to explore are hls4ml and finn. These are some frameworks which can achieve low latency inference of neural networks on FPGAs using Xilinx Vitis HLS. These are what I found when I did a similar experiment but with much lower latency target (a few hundred ns) and a very simple MLP with 1D signal as input which was a year ago. Not sure if there are better alternatives available as of 2023. But conceptually all these work on the primary principle of having a supporting framework/methodology to first quantize the network and limit the precision of data to fixed point. The HLS then produced will also be a result of the framework applying dataflow techniques such that the resulting HLS code will produce an RTL which has the best overall latency.
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looking for resources to design a basic deep learning feed forward accelerator
Check hls4ml. Developed by CERN for fast classification in FPGA for high-energy physics experiments.
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How to build FPGA-based ML accelerator?
I would check out hls4ml. It's an open source project made by/for people at CERN to convert neural networks created in Python using QKeras (a quantization extension of Keras) into HLS, with Vivado HLS being the most well supported. There are some caveats though, and a fellow student and I have had trouble getting the generated HLS to match the Keras model and be feasible to synthesize, but it seems to work well for smaller neural networks.
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How are TensorFlow Models implemented on PYNQ's PS & PL
Since you're looking for PL-only implementation, HLS4ML may fit your needs. It was developed to port TensorFlow models directly to FPGAs in particle physics experiments. Current development allows for implementation on SoC and MPSoC, though.
- Open source projects?
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A note from our sponsor - WorkOS
workos.com | 25 Apr 2024
Stats
Basic hls4ml repo stats
11
1,103
9.1
6 days ago
fastmachinelearning/hls4ml is an open source project licensed under Apache License 2.0 which is an OSI approved license.
The primary programming language of hls4ml is C++.
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