Steven J. E. Wilton

Daniel H. Noronha, Bahar Salehpour, Steven J. E. Wilton

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.

Jeffrey Goeders, Steven J. E. Wilton

High-Level Synthesis (HLS) is emerging as a mainstream design methodology, allowing software designers to enjoy the benefits of a hardware implementation. Significant work has led to effective compilers that produce high-quality hardware designs from software specifications. However, in order to fully benefit from the promise of HLS, a complete ecosystem that provides the ability to analyze, debug, and optimize designs is essential. This ecosystem has to be accessible to software designers. This is challenging, since software developers view their designs very differently than how they are physically implemented on-chip. Rather than individual sequential lines of code, the implementation consists of gates operating in parallel across multiple clock cycles. In this paper, we report on our efforts to create an ecosystem that allows software designers to debug HLS-generated circuits in a familiar manner. We have implemented our ideas in a debug framework that will be included in the next release of the popular LegUp high-level synthesis tool.