Resource Aware Autonomy on End Devices by Dr Bryan Donyanavard

Abstract

As computing capacity of modern embedded systems increases, emerging applications become more complex, demanding more performance from battery‑powered end‑devices. To effectively maximize utilization of end‑devices, we deploy system intelligence in order to manage resources at runtime. I will illustrate intelligent self‑management at different layers of the system stack in the context of realtime autonomous applications through two recent projects: (a) approximate visual simultaneous localization and mapping, and (b) near‑memory data compression for autonomy pipelines.

Biography

Bryan is an Assistant Professor in the Computer Science department at San Diego State University. Prior to that, he was a researcher in the IoT and Cyber‑physical Systems group at Ericsson in Stockholm. His research focuses on runtime management of resource‑constrained systems in software and architecture. He received his PhD in Computer Science from UC Irvine in 2019. He has previously worked as a software engineer at Sun Microsystems and Google, and spent time as a visiting researcher at TU Munich.