Spatial: A Language and Compiler for Application Accelerators
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 demonstrate the language's ability to target FPGAs and CGRAs from common source code. We show that applications written in Spatial are, on average, 42% shorter and achieve a mean speedup of 2.9x over SDAccel HLS when targeting a Xilinx UltraScale+ VU9P FPGA on an Amazon EC2 F1 instance.
Thu 21 JunDisplayed time zone: Eastern Time (US & Canada) change
11:00 - 12:15 | Multicore and MorePLDI Research Papers at Grand Ballroom AB Chair(s): Yannis Smaragdakis University of Athens | ||
11:00 25mTalk | Spatial: A Language and Compiler for Application Accelerators PLDI Research Papers David Koeplinger Stanford University, USA, Matthew Feldman Stanford University, USA, Raghu Prabhakar Stanford University, USA, Yaqi Zhang Stanford University, USA, Stefan Hadjis Stanford University, USA, Ruben Fiszel EPFL, Switzerland, Tian Zhao Stanford University, Luigi Nardi Stanford University, Ardavan Pedram Stanford University, USA, Christos Kozyrakis Stanford University, USA, Kunle Olukotun Stanford University Media Attached | ||
11:25 25mTalk | Enhancing Computation-to-Core Assignment with Physical Location Information PLDI Research Papers Orhan Kislal Pennsylvania State University, USA, Jagadish Kotra Pennsylvania State University, USA, Xulong Tang Penn State, Mahmut Taylan Kandemir University of Pennsylvania, Myoungsoo Jung Yonsei University, South Korea Media Attached | ||
11:50 25mTalk | SWOOP: Software-Hardware Co-design for Non-speculative, Execute-Ahead, In-Order Cores PLDI Research Papers Kim-Anh Tran Uppsala University, Sweden, Alexandra Jimborean Uppsala University, Trevor E. Carlson National University of Singapore, Konstantinos Koukos Uppsala University, Sweden, Magnus Själander Norwegian University of Science and Technology (NTNU), Stefanos Kaxiras Uppsala University, Sweden Media Attached | ||