From 9453683247b49d3582a923a00dbebecd30906ddb Mon Sep 17 00:00:00 2001 From: auphelia Date: Wed, 31 Jan 2024 15:47:22 +0000 Subject: [PATCH] [gh-pages] Restructure pages --- docs/_layouts/default.html | 1 + docs/about.md | 29 --------------------------- docs/img/finn-example.png | Bin 25431 -> 1292932 bytes docs/index.md | 40 +++++++++++++++++++++++++++---------- docs/quickstart.md | 30 ++++++++++++++++++++++++++++ 5 files changed, 60 insertions(+), 40 deletions(-) create mode 100644 docs/quickstart.md diff --git a/docs/_layouts/default.html b/docs/_layouts/default.html index c4baa1c043..586ffb9c1c 100644 --- a/docs/_layouts/default.html +++ b/docs/_layouts/default.html @@ -27,6 +27,7 @@
  • Download TAR
  • {% endif %}
  • About
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  • Quickstart
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  • diff --git a/docs/about.md b/docs/about.md index d9cb584b35..64d27f19ec 100644 --- a/docs/about.md +++ b/docs/about.md @@ -1,29 +1,3 @@ -## What is FINN? - -drawing - -FINN is an ML framework by the Integrated Communications and AI Lab of AMD Research & Advanced Development. -It provides an end-to-end flow for the exploration and implementation of quantized neural network inference solutions on FPGAs. -FINN generates dataflow architectures as a physical representation of the implemented custom network in space. -It is not a generic DNN acceleration solution but relies on co-design and design space exploration for quantization and parallelization tuning so as to optimize a solutions with respect to resource and performance requirements. -
    -## Features - -* **Templated Vitis HLS library of streaming components:** FINN comes with an -HLS hardware library that implements convolutional, fully-connected, pooling and -LSTM layer types as streaming components. The library uses C++ templates to -support a wide range of precisions. -* **Ultra low-latency and high performance -with dataflow:** By composing streaming components for each layer, FINN can -generate accelerators that can classify images at sub-microsecond latency. -* **Many end-to-end example designs:** We provide examples that start from training a -quantized neural network, all the way down to an accelerated design running on -hardware. The examples span a range of datasets and network topologies. -* **Toolflow for rapid design generation:** The FINN toolflow supports allocating -separate compute resources per layer, either automatically or manually, and -generating the full design for synthesis. This enables rapid exploration of the -design space. - ## Who are we? The FINN team consists of members of AMD Research under Ralph Wittig (AMD Research & Advanced Development) and members of Custom & Strategic Engineering under Allen Chen, working very closely with the Pynq team. @@ -37,6 +11,3 @@ Thomas Preusser, Jakoba Petri-Koenig, Ken O’Brien From top left to bottom right: Eamonn Dunbar, Kasper Feurer, Aziz Bahri, John Monks, Mirza Mrahorovic - - - diff --git a/docs/img/finn-example.png b/docs/img/finn-example.png index b850adcf3bf08431875f2af46787b56ef2c6e57c..ab748851ea1dcacdf0a8bbc1bfe6584e9c2e7d00 100644 GIT binary patch literal 1292932 zcmeFZd05hE+c(}!HBB|uOlHnhX3eCQrcRlfnA&EPrba3*Xr$&YnhTf<*w|ueVr6Qn zXldq(E8;G#nwpp^A|mB3DDI#j@LtUQJonhl{meV>pTFbxa2&G$#rJz%*SUPo&-ppO zcQ2ShH*MIlVb!Wto6em*eR0*Q@7SwWeUtg^H{cV!3-!`3z6!VqJ-MoYx@#P~S>txX z^u(%F#jzWg9oK^Q>#v@*4Oq46@Js1GU+uqQ$5^#Wl6mg*3CnAjCYbB&t;Puwzv_#R zUR=j2HD1wae1V!ZXYH#_CXZv|n2*kxYoptR`{K0Ez0e(;H{eCcp8o2G_}qxKYq!aM zWwcGzrbsMq<$vBix>;9qHC}}4FQoC;mJ`HK|j_u-}1u_|xm<#xt;WfBFo( 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