The next wave of pervasive AI pushes machine learning (ML) acceleration toward the extreme edge, with mW powerbudgets, while atthe same time it raisesthebar in terms of accuracy and capabilities, with new ML models being propose on a daily basis. To succeed in this balancing act, we need principled ways to walk the line between flexible and highly specialized ML acceleration architectures. In this talk I will detail on how to walk the line, drawing from the experience of the open PULP (Parallel Ultra-Low Power) platform, based on ML-enhanced RISC-V processors coupled with domain-specific acceleration engines.
PULP: Extreme Energy Efficiency for Extreme Edge AI Acceleration / Benini, Luca. - ELETTRONICO. - (2022), pp. 1-1. (Intervento presentato al convegno 2022 11th Mediterranean Conference on Embedded Computing (MECO) tenutosi a Budva, Montenegro nel 7-10 June 2022) [10.1109/MECO55406.2022.9797094].
PULP: Extreme Energy Efficiency for Extreme Edge AI Acceleration
Benini, Luca
2022
Abstract
The next wave of pervasive AI pushes machine learning (ML) acceleration toward the extreme edge, with mW powerbudgets, while atthe same time it raisesthebar in terms of accuracy and capabilities, with new ML models being propose on a daily basis. To succeed in this balancing act, we need principled ways to walk the line between flexible and highly specialized ML acceleration architectures. In this talk I will detail on how to walk the line, drawing from the experience of the open PULP (Parallel Ultra-Low Power) platform, based on ML-enhanced RISC-V processors coupled with domain-specific acceleration engines.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.