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+---
+layout: post
+title:  "FINN v0.2b (beta) is released"
+author: "Yaman Umuroglu"
+---
+
+We've been working on the new version of the FINN compiler for a while, and today we are excited to announce our first beta release to 
+give you a taste of how things are shaping up! 
+
+Here's a quick overview of the key features:
+
+* <b>Train and export highly-quantized networks in PyTorch using Brevitas.</b> You can use <a href="https://github.com/Xilinx/brevitas">Brevitas</a>,
+  our PyTorch library for quantization-aware training to train networks with few-bit weights and activations, then export them into 
+  FINN-ONNX to be used by the FINN compiler.
+
+* <b>Fully transparent end-to-end flow.</b> We support taking quantized networks (with limitations, see bottom of post) all the way down to a 
+  customized FPGA bitstream. This happens across many steps ranging from streamlining to Vivado IPI stitching, and each step is fully 
+  visible to the user. So if you are happy with just the threshold-activation (streamlined) QNN in ONNX, or if you want to take the 
+  generated Vivado IP block and integrate it into your own IPI design, it's easy to break out of the flow at any step. 
+  We also provide a variety of mechanisms to verify the design at different steps.
+
+* <b>ONNX-based intermediate representation.</b> We use ONNX with some custom nodes and annotations as our intermediate representation. As the 
+  FINN compiler transforms the network across many steps to produce an FPGA bitstream, you can view and explore the transformed network 
+  using the excellent <a href="https://www.lutzroeder.com/ai/netron">Netron</a> viewer from the comfort of your web browser.
+
+* Tutorials and documentation. We have prepared a set of <a href="https://github.com/Xilinx/finn/tree/master/notebooks">Jupyter notebooks</a> 
+  to let you experiment with some of the things FINN can do, covering the basics, demonstrating the end-to-end flow on an example network, 
+  and discussing some of the internals for more advanced users and developers. We also have Sphinx-generated documentation on 
+  <a href="http://finn.readthedocs.io/">readthedocs</a> for more information on the FINN compiler and its API.
+
+The release (tagged 0.2b) is now available on GitHub. Currently it's a beta release and only supports fully-connected layers in linear 
+(non-branching) topologies, but we're actively working on the end-to-end convolution support for the next release. Further down the 
+road, we hope to support more advanced topologies and provide end-to-end examples for MobileNet and ResNet-50.