Work

Compiler-aware AI Hardware Design for Edge Devices

Year: 2025

Type: conference-paper

Abstract: The adoption of novel AI hardware is often hindered by the lack of available deployment solutions. To address this challenge, multi-target deep learning (DL) compilers have emerged, offering a large variety of optimizations and automated deployment solutions that bridge the gap between AI software and hardware. However, support for dedicated AI accelerators remains limited due to their vast architectural differences and unique characteristics.

DOI: 10.1145/3721888.3722095

Authors: Paul Palomero Bernardo, Patrick Schmid, Christoph Gerum, Oliver Bringmann

Institution: University of Tübingen

Language: English

Topic: Embedded Systems Design Techniques

Subfield: Hardware and Architecture

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

OpenAlex ID: W4409001572

FWCI: 1.856

Cites: 22

Cited by: 2

Related to: 10