  Recent work has shown that Field-Programmable Gate Arrays (FPGAs) play an
important role in the acceleration of Machine Learning applications. Initial
specification of machine learning applications are often done using a
high-level Python-oriented framework such as Tensorflow, followed by a manual
translation to either C or RTL for synthesis using vendor tools. This manual
translation step is time-consuming and requires expertise that limit the
applicability of FPGAs in this important domain. In this paper, we present an
open-source tool-flow that maps numerical computation models written in
Tensorflow to synthesizable hardware. Unlike other tools, which are often
constrained by a small number of inflexible templates, our flow uses Google's
XLA compiler which emits LLVM code directly from a Tensorflow specification.
This LLVM code can then be used with a high-level synthesis tool to
automatically generate hardware. We show that our flow allows users to generate
Deep Neural Networks with very few lines of Python code.
