Plaster: an Embedded FPGA-based Cluster Orchestrator for Accelerated Distributed Algorithms

The increasing use of real-time data-intensive applications and the growing interest in Heterogeneous Architectures have led to the need for increasingly complex embedded computing systems. An example of this is the research carried out by both the scientific community and companies toward embedded multi-FPGA systems for the implementation of the inference phase of Convolutional Neural Networks.In this paper, we focus on optimizing the management system of these embedded FPGA-based distributed systems. We extend the state-of-the-art FARD framework to data-intensive applications in an embedded scenario. Our orchestration and management infrastructure benefits from compiled language and is accessible to end-users by the means of Python APIs, which provides a simple way to interact with the cluster and design apps to run on the embedded nodes. The proposed prototype system consists of a PYNQ-based cluster of multiple FPGAs and has been evaluated by running an FPGA-based You Only Look Once (YOLO) image classification algorithm.