Designing efficient, application-specialized hardware accelerators requires assessing trade-offs between
a hardware module's performance and resource requirements.
To facilitate hardware design space exploration,
we describe Aetherling, a system for automatically compiling data-parallel programs into statically scheduled, streaming hardware circuits.
Aetherling contributes a space- and time-aware intermediate language featuring data-parallel operators that represent parallel or sequential hardware modules,
and sequence data types that encode a module's throughput by specifying when sequence elements are produced or consumed.
As a result, well-typed operator composition in the space-time language corresponds to connecting hardware modules via statically scheduled, streaming interfaces.
We provide rules for transforming programs written in a standard data-parallel language (that carries no information about hardware implementation)
into equivalent space-time language programs.
We then provide a scheduling algorithm that searches over the space of transformations to quickly generate area-efficient hardware designs that achieve a programmer-specified throughput.
Using benchmarks from the image processing domain, we demonstrate that Aetherling enables rapid exploration of hardware designs with different throughput and area characteristics, and yields results that require 1.8-7.9$\times$ fewer FPGA slices than those of prior hardware generation systems.
Conference DayThu 18 JunDisplayed time zone: Pacific Time (US & Canada) change
09:20 - 10:20 | Type SystemsPLDI Research Papers at PLDI Research Papers live stream Chair(s): Arjun GuhaNortheastern University | ||
09:20 20mTalk | Predictable Accelerator Design with Time-Sensitive Affine Types PLDI Research Papers Rachit NigamCornell University, USA, Sachille AtapattuCornell University, USA, Samuel ThomasCornell University, USA, Zhijing LiCornell University, USA, Theodore BauerCornell University, USA, Yuwei YeCornell University, USA, Apurva KotiCornell University, USA, Adrian SampsonCornell University, USA, Zhiru ZhangCornell University, USA | ||
09:40 20mTalk | Type-Directed Scheduling of Streaming Accelerators PLDI Research Papers David DurstStanford University, USA, Matthew FeldmanStanford University, USA, Dillon HuffStanford University, USA, David AkeleyUniversity of California at Los Angeles, USA, Ross DalyStanford University, USA, Gilbert Louis BernsteinUniversity of California at Berkeley, USA, Marco PatrignaniStanford University, USA / CISPA, Germany, Kayvon FatahalianStanford University, USA, Pat HanrahanStanford University, USA | ||
10:00 20mTalk | FreezeML: Complete and Easy Type Inference for First-Class Polymorphism PLDI Research Papers Frank EmrichUniversity of Edinburgh, UK, Sam LindleyHeriot-Watt University, UK / The University of Edinburgh, UK / Imperial College London, UK, Jan StolarekUniversity of Edinburgh, UK, James CheneyUniversity of Edinburgh, UK, Jonathan CoatesUniversity of Edinburgh, UK | ||