The Magnificent Seven Challenges and Opportunities in Domain-Specific Accelerator Design for Autonomous Systems

TL;DR

This paper identifies seven key challenges in designing domain-specific accelerators for autonomous systems, highlighting opportunities and guiding principles for architects to innovate effectively in this evolving field.

Contribution

It introduces the Magnificent Seven Challenges framework, providing a structured understanding of hurdles and opportunities in domain-specific accelerator design, especially for autonomous systems.

Findings

Identified seven core challenges in domain-specific accelerator design.

Analyzed these challenges through the lens of autonomous systems.

Outlined opportunities for overcoming challenges and advancing the field.

Abstract

The end of Moore's Law and Dennard Scaling has combined with advances in agile hardware design to foster a golden age of domain-specific acceleration. However, this new frontier of computing opportunities is not without pitfalls. As computer architects approach unfamiliar domains, we have seen common themes emerge in the challenges that can hinder progress in the development of useful acceleration. In this work, we present the Magnificent Seven Challenges in domain-specific accelerator design that can guide adventurous architects to contribute meaningfully to novel application domains. Although these challenges appear across domains ranging from ML to genomics, we examine them through the lens of autonomous systems as a motivating example in this work. To that end, we identify opportunities for the path forward in a successful domain-specific accelerator design from these challenges.