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.
Thu 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 Guha Northeastern University | ||
09:20 20mTalk | Predictable Accelerator Design with Time-Sensitive Affine Types PLDI Research Papers Rachit Nigam Cornell University, USA, Sachille Atapattu Cornell University, USA, Samuel Thomas Cornell University, USA, Zhijing Li Cornell University, USA, Theodore Bauer Cornell University, USA, Yuwei Ye Cornell University, USA, Apurva Koti Cornell University, USA, Adrian Sampson Cornell University, USA, Zhiru Zhang Cornell University, USA | ||
09:40 20mTalk | Type-Directed Scheduling of Streaming Accelerators PLDI Research Papers David Durst Stanford University, USA, Matthew Feldman Stanford University, USA, Dillon Huff Stanford University, USA, David Akeley University of California at Los Angeles, USA, Ross Daly Stanford University, USA, Gilbert Bernstein University of California at Berkeley, USA, Marco Patrignani Stanford University, USA / CISPA, Germany, Kayvon Fatahalian Stanford University, USA, Pat Hanrahan Stanford University, USA | ||
10:00 20mTalk | FreezeML: Complete and Easy Type Inference for First-Class Polymorphism PLDI Research Papers Frank Emrich University of Edinburgh, UK, Sam Lindley Heriot-Watt University, UK / The University of Edinburgh, UK / Imperial College London, UK, Jan Stolarek University of Edinburgh, UK, James Cheney University of Edinburgh, UK, Jonathan Coates University of Edinburgh, UK | ||