Advanced Dataflow Programming using Actor Machines for High-Level Synthesis

Abstract

The use of parallelism has increased drastically in recent years. Parallel platforms come in many forms: multi-core processors, embedded hybrid solutions such as multi-processor system-on-chip with reconfigurable logic, and cloud datacenters with multi-core and reconfigurable logic. These heterogeneous platforms can offer massive parallelism, but it can be difficult to exploit, particularly when combining solutions constructed with multiple architectures. To program a heterogeneous platform, a developer must master different programming languages, tools, and APIs to program each aspect of platform separately and then must find a means to connect them with communication interfaces. The motivation of this work is to provide a single programming model and framework for hardware-software stream programs on heterogeneous platforms. Our framework, StreamBlocks, starts with a dataflow programming model for both embedded and datacenter platforms. Dataflow programming is an alternative model of computation that captures both data and task parallelism. We describe a compiler infrastructure for CAL dataflow programs for hardware code generation. CAL is a dataflow programming language that can express multiple dataflow models of computation. StreamBlocks is based on the Tycho compiler infrastructure, which transforms each actor in a dataflow program to an abstract machine model, called Actor Machine. Actor Machines provides a unified model for executing actors in both hardware and software and permit our compiler extension and backend to generate efficient FPGA code. Unlike other systems, the programming model and compiler directly support hardware-software systems in which an FPGA functions as a coprocessor to a CPU. This permits easy integration with existing workflows.