finn VS deepsocflow

Compare finn vs deepsocflow and see what are their differences.

finn

Dataflow compiler for QNN inference on FPGAs (by Xilinx)
Dataflow quantization Fpga Compiler neural-network
Source Code
xilinx.github.io
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deepsocflow

An Open Workflow to Build Custom SoCs and run Deep Models at the Edge (by abarajithan11)
AI axi-stream Cnn Dnn Fpga neural-network Verilog Asic asic-design hls4ml Systemverilog
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finn deepsocflow
4 1
665 36
3.2% -
9.7 9.1
10 days ago 10 days ago
Python Python
BSD 3-clause "New" or "Revised" License Apache License 2.0
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finn

Posts with mentions or reviews of finn. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-06-13.
  • Hi, What could be the best HLS tool for implementing neural networks on FPGA
    2 projects | /r/FPGA | 13 Jun 2023
    FINN - https://github.com/Xilinx/finn
  • Can anyone tell if Xilinx's FINN (from Xilinx's research lab) is restricted for use only to xilinx based FPGAs?
    2 projects | /r/FPGA | 8 Apr 2023
    Seems fine to use on other FPGAs, there are some clauses you need to abide by. https://github.com/Xilinx/finn/blob/main/LICENSE.txt
  • Sub ms - 3ms Latency Vision task on FPGA
    2 projects | /r/FPGA | 5 Feb 2023
    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.

deepsocflow

Posts with mentions or reviews of deepsocflow. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing finn and deepsocflow you can also consider the following projects:

hls4ml - Machine learning on FPGAs using HLS

FPGA-Video-Processing - Realtime video processing w/ Gaussian + Sobel Filters targeting Artix-7 FPGA

intel-extension-for-pytorch - A Python package for extending the official PyTorch that can easily obtain performance on Intel platform

qkeras - QKeras: a quantization deep learning library for Tensorflow Keras

finn vs hls4ml deepsocflow vs FPGA-Video-Processing finn vs intel-extension-for-pytorch finn vs qkeras