Spatial: A language and compiler for application accelerators
David Koeplinger
Matthew Feldman
Yaqi Zhang
Stefan Hadjis
Ruben Fiszel
Tian Zhao
Luigi Nardi
Ardavan Pedram
Kunle Olukotun
Stanford
ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI), 2018
Abstract
Industry is increasingly turning to reconfigurable architectures like FPGAs and CGRAs for improved performance and energy efficiency. Unfortunately, adoption of these architectures has been limited by their programming models. HDLs lack abstractions for productivity and are difficult to target from higher level languages. HLS tools are more productive, but offer an ad-hoc mix of software and hardware abstractions which make performance optimizations difficult.In this work, we describe a new domain-specific language and compiler called Spatial for higher level descriptions of application accelerators. We describe Spatial’s hardware-centric abstractions for both programmer productivity and design performance, and summarize the compiler passes required to support these abstractions, including pipeline scheduling, automatic memory banking, and automated design tuning driven by active machine learning. We …