Invited: Achieving PetaOps/W Edge-AI Processing

Manil Dev Gomony, Bas Ahn, Rick Luiken, Yashvardhan Biyani, Anteneh Gebregiorgis, Axel Laborieux, Friedemann Zenke, Said Hamdioui, Henk Corporaal*

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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Abstract

Artificial Intelligence (AI) supported by Deep Artificial Neural Networks (ANNs) is booming and already used in many applications, with impressive results, and we are still its infancy. For many sensing applications it would be advantageous if we could move AI from cloud to Edge. However this requires huge improvements in energy-efficiency. The CONVOLVE project (convolve.eu) aims at enabling smart edge devices through a concerted effort at all layers of the design stack. This ranges from using much more efficient models and mappings, like exploiting Spiking Neural Networks (SNNs), to new processing architectures, like compute-in-memory (CIM), use of approximation, and using new device technology, like memristors. However these latter changes make HW more susceptible to noise and other disturbances. Online continuous learning (i.e. adapting weights) may alleviate these problems. This paper shows several CONVOLVE developments in the crucial areas of CIM architectures, SNN accelerators and online learning.

Original languageEnglish
Title of host publicationProceedings of the 61st ACM/IEEE Design Automation Conference, DAC 2024
PublisherIEEE
Number of pages4
ISBN (Electronic)9798400706011
DOIs
Publication statusPublished - 2024
Event61st ACM/IEEE Design Automation Conference, DAC 2024 - San Francisco, United States
Duration: 23 Jun 202427 Jun 2024

Publication series

NameProceedings - Design Automation Conference
ISSN (Print)0738-100X

Conference

Conference61st ACM/IEEE Design Automation Conference, DAC 2024
Country/TerritoryUnited States
CitySan Francisco
Period23/06/2427/06/24

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