@article {1435523,
	title = {Learnings from a HLS-based High-Productivity Digital VLSI Flow},
	journal = {In Workshop on Languages, Tools, and Techniques for Accelerator Design (LATTE{\textquoteright}22)},
	year = {2022},
	abstract = {Thetwilight of Dennardscalinghasactivatedaglobaltrendtowards application-based hardware specialization. This trend is currently accelerating due to the surging democratization and deployment of machine learning on mobile and IoT compute platforms. At the same time, the growing complexity of specialized system-on-chips (SoCs) is levying a more laborious tax on ASIC companies{\textquoteright} design and verification efforts. High-level synthesis (HLS) is emerging as a foremost agile VLSI development methodology gaining increasing adoption in the hardware design community. However, concerns over Quality of Results (QoR) remain a key factor inhibiting more mainstream adoption of HLS. Obtaining optimal PPA outcomes can sometimes be an elusive or challenging task and strongly correlates with the syntactic approach of the high-level source code. In this paper, we aim to share the proven HLS practices we employed to raise the level of confidence in the post-silicon functional and performance expectations from our accelerator designs. In doing so, we recount some of the main challenges we encountered in our HLS-based hardware-software co-design journey and offer a few recommendations cultivated from our learnings. Finally, we posit on wheretheresearch opportunities to further improve design QoR and HLS user experience lie.},
	url = {https://capra.cs.cornell.edu/latte22/paper/6.pdf},
	author = {Tambe, Thierry and David Brooks and Gu-Yeon Wei}
}
