Publikation

FLOWER: A Comprehensive Dataflow Compiler for High-Level Synthesis

Puya Amiri; Arsène Pérard-Gayot; Richard Membarth; Philipp Slusallek; Roland Leißa; Sebastian Hack

In: Proceedings of the 2021 International Conference on Field Programmable Technology (ICFPT). International Conference on Field Programmable Technology (FPT-2021), December 6-10, Auckland, New Zealand, Pages 1-9, IEEE, 12/2021.

Zusammenfassung

FPGAs have found their way into data centers as accelerator cards, making reconfigurable computing more accessible for high-performance applications. At the same time, new high-level synthesis compilers like Xilinx Vitis and runtime libraries such as XRT attract software programmers into the reconfigurable domain. While software programmers are familiar with task-level and data-parallel programming, FPGAs often require different types of parallelism. For example, data-driven parallelism is mandatory to obtain satisfactory hardware designs for pipelined dataflow architectures. However, software programmers are often not acquainted with dataflow architectures - resulting in poor hardware designs. In this work we present FLOWER, a comprehensive compiler infrastructure that provides automatic canonical transformations for high-level synthesis from a domain-specific library. This allows programmers to focus on algorithm implementations rather than low-level optimizations for dataflow architectures. We show that FLOWER allows to synthesize efficient implementations for high-performance streaming applications targeting System-on-Chip and FPGA accelerator cards, in the context of image processing and computer vision.

Projekte

HP-DLF - High Performance Deep Learning Framework,
REACT - Autonomes Fahren: Modellierungs-, Lern- und Simulationsumgebung für das Fußgängerverhalten in kritischen Verkehrssituationen,
HorME - Horizontally scalable, Multipurpose execution Environment,
MetaDL - AI Lab for Metaprogramming in Deep Learning