hls4ml
srs
hls4ml | srs | |
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11 | 10 | |
1,110 | 24,249 | |
3.5% | 0.9% | |
9.2 | 8.5 | |
7 days ago | 6 days ago | |
C++ | C++ | |
Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
hls4ml
- 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project that originated at Fermilab https://github.com/fastmachinelearning/hls4ml (based on Xilinx Vivado which has been replaced by Vitis)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?
srs
- What's the state of screen-sharing games to friends on linux?
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Ever wanted to be an exhibitor at Virtual Market? There's only 2 days left to apply!
With OBS supporting HEVC/H.265 (and AV1, VP9) as output codec in a recent beta, I've been testing VRChat support. Keeping eye on https://github.com/ossrs/srs/pull/3495 before I try HEVC livestreams into VRChat as I believe I can try RTSP and mpeg-ts.
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Live Streaming
For in-house hosting, a project like https://github.com/ossrs/srs or https://github.com/illuspas/Node-Media-Server may meet your needs. Ultimately though, you should be able to build whatever you need from scratch using an Nginx server with the RTMP module and an ffmpeg process
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Adding WebRTC support to OBS using Rust
I don't know the answer to the WebRTC part, but as long as you have a server with not-outrageously-priced outbound bandwidth, you can install an open source RTMP server like SRS[1], and stream to that RTMP server from OBS. It's really easy, configure the RTMP server & stream key, then "Start Streaming" which is right next to "Start Recording". You can then hand your friends a link, and they can play it in any media player with RTMP/HLS/FLV stream support, or you can add a simple web UI with e.g. hls.js[2] (very easy to write, there might even be prepackaged solutions) so that they truly don't need to download anything.
[1] https://github.com/ossrs/srs
[2] https://github.com/video-dev/hls.js/
- Ask HN: FFmpeg real-time desktop streaming
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Video Streaming : HTTP Real Time Streaming using "multipart/x-mixed-replace" or WebRTC server-to-client?
not flask related but SRS is my go to when i think about media/video streaming
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Can anybody help me understand how to uninstall a git clone I did
from the configure file, it looks like there should be a make destroy command which basically just removes everything in /objs: https://github.com/ossrs/srs/blob/4.0release/trunk/configure
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[TASK] Document setting up HLS streaming service using StreamNight, SRS, Caddy, and OBS - $25
SRS
- ossrs/srs
What are some alternatives?
qkeras - QKeras: a quantization deep learning library for Tensorflow Keras
Ant-Media-Server - Ant Media Server is a live streaming engine software that provides adaptive, ultra low latency streaming by using WebRTC technology with ~0.5 seconds latency. Ant Media Server is auto-scalable and it can run on-premise or on-cloud.
Silice - Silice is an easy-to-learn, powerful hardware description language, that simplifies designing hardware algorithms with parallelism and pipelines.
SRT-Stats-Monitor - Loopy SRT Stats Monitor. Monitors your OBS SRT, SLS, BELABOX, RESTREAMER, RIST, & NGINX connection/s and switches OBS scene on a failed connection. Ideal for IRL/live streaming.
v4l2rtspserver - RTSP Server for V4L2 device capture supporting HEVC/H264/JPEG/VP8/VP9
docker-nginx-rtmp - 🐋 A Dockerfile for nginx-rtmp-module + FFmpeg from source with basic settings for streaming HLS. Built on Alpine Linux.
PipelineC - A C-like hardware description language (HDL) adding high level synthesis(HLS)-like automatic pipelining as a language construct/compiler feature.
OvenMediaEngine - OvenMediaEngine (OME) is a Sub-Second Latency Live Streaming Server with Large-Scale and High-Definition. #WebRTC #LLHLS
fastocloud_com - Self-hosted IPTV/NVR/CCTV/Video service (Community version) [Moved to: https://github.com/fastogt/fastocloud]
media-server-node - WebRTC Media Server for Node.js
fastocloud_mpart - Self-hosted IPTV/NVR/CCTV/Video service [Moved to: https://github.com/fastogt/fastocloud]
srsRAN_4G - Open source SDR 4G software suite from Software Radio Systems (SRS) https://docs.srsran.com/projects/4g