@inproceedings{349fcfa9411c4f00964434ef6a9646ee,
title = "HPVM2FPGA: Enabling True Hardware-Agnostic FPGA Programming",
abstract = "Current FPGA programming tools require extensive hardware-specific manual code tuning to achieve performance, which is intractable for most software application teams. We present HPVM2FPGA, a novel end-to-end compiler and auto-tuning system that can automatically tune hardware-agnostic programs for FPGAs. HPVM2FPGA uses a hardware-agnostic abstraction of parallelism as an intermediate representation (IR) to represent hardware-agnostic programs. HPVM2FPGA's powerful optimization framework uses sophisticated compiler optimizations and design space exploration (DSE) to automatically tune a hardware-agnostic program for a given FPGA. HPVM2FPGA is able to support software programmers by shifting the burden of performing hardware-specific optimizations to the compiler and DSE. We show that HPVM2FPGA can achieve up to 33×speedup compared to unoptimized baselines and can match the performance of hand-tuned HLS code for three of four benchmarks. We have designed HPVM2FPGA to be a modular and extensible framework, and we expect it to match hand-tuned code for most programs as the system matures with more optimizations. Overall, we believe that it constitutes a solid step closer to fully hardware-agnostic FPGA programming, making it a suitable cornerstone for future FPGA compiler research.",
keywords = "FPGA, High-level synthesis, compilers for FPGA, hardware-agnostic FPGA programming",
author = "Adel Ejjeh and Leon Medvinsky and Aaron Councilman and Hemang Nehra and Suraj Sharma and Vikram Adve and Luigi Nardi and Eriko Nurvitadhi and Rutenbar, \{Rob A.\}",
note = "This work was supported in part by funding from an Intel Research Award, from IBM under the DARPA DSSoC program, and from the University of Illinois. Luigi Nardi was partly supported by the Wallenberg AI, Autonomous Systems and Software program (WASP) funded by the Knut and Alice Wallenberg Foundation. Nardi s research was also supported in part by affiliate members and other supporters of the Stanford DAWN project - Ant Financial, Facebook, Google, InfoSys, Teradata, NEC, and VMware; 33rd IEEE International Conference on Application-Specific Systems, Architectures and Processors, ASAP 2022 ; Conference date: 12-07-2022 Through 14-07-2022",
year = "2022",
doi = "10.1109/ASAP54787.2022.00012",
language = "English (US)",
series = "Proceedings of the International Conference on Application-Specific Systems, Architectures and Processors",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1--10",
editor = "Miquel Pericas and Pnevmatikatos, \{Dionisios N.\} and Trancoso, \{Pedro Petersen Moura\} and Ioannis Sourdis",
booktitle = "Proceedings - 2022 IEEE 33rd International Conference on Application-Specific Systems, Architectures and Processors, ASAP 2022",
address = "United States",
}