Show HN: Open source machine learning inference accelerators on FPGA

Tom from Tensil here - happy to answer questions!<p>We developed Tensil to bring custom ML accelerators to people who don&#x27;t have the resources of companies like Google, Facebook and Tesla. Currently, we&#x27;re focused on supporting convolutional neural network inference on edge FPGA (field programmable gate array) platforms, but we aim to support all model architectures on a wide variety of fabrics for both training and inference.<p>Tensil is different from other ML accelerators in that it is open source and really easy to use. For example, you can generate a custom accelerator with one command:<p><pre><code> $ tensil rtl --arch &lt;my_architecture&gt; </code></pre> You can compile your ML model targeting that accelerator like so:<p><pre><code> $ tensil compile --arch &lt;my_architecture&gt; --model &lt;my_model&gt;` </code></pre> Running your model on FPGA is as simple as doing the following:<p><pre><code> $ tcu.load_model(&lt;compiled_model&gt;) $ outputs = tcu.run(inputs) </code></pre> The accelerator generator was developed in Chisel and we built our own parametrizable compiler to target it. The link in the post takes you to the documentation, and here&#x27;s a link to the Github repository: <a href="https:&#x2F;&#x2F;github.com&#x2F;tensil-ai&#x2F;tensil&#x2F;" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;tensil-ai&#x2F;tensil&#x2F;</a>