Accelerator Real-time Edge AI for Distributed Systems (READS) (Proposal)

Seiya, K.; Hazelwood, Kyle; Ibrahim, Kamarulzaman; Nagaslaev, V.; Nicklaus, D.; A., Schupbach, B.; Thurman‐Keup, R.; Tran, N. V.; Liu, H.; Memik, Seda Öǧrenci

doi:10.2172/1769391

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…One to use ML to help regulate slow spill in the Delivery ring to Mu2e, and another to develop a real-time online model to de-blend losses coming from the Recycler and Main Injector accelerators which share an enclosure. Both READS projects will make use of FPGA online ML models for inference and will collect data at low latencies from distributed systems around the accelerator complex (Seiya et al, 2021 ).…”

Section: Exemplars Of Domain Applicationsmentioning

confidence: 99%

Applications and Techniques for Fast Machine Learning in Science

Deiana¹,

Tran²,

Agar³

et al. 2022

Front. Big Data

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In this community review report, we discuss applications and techniques for fast machine learning (ML) in science—the concept of integrating powerful ML methods into the real-time experimental data processing loop to accelerate scientific discovery. The material for the report builds on two workshops held by the Fast ML for Science community and covers three main areas: applications for fast ML across a number of scientific domains; techniques for training and implementing performant and resource-efficient ML algorithms; and computing architectures, platforms, and technologies for deploying these algorithms. We also present overlapping challenges across the multiple scientific domains where common solutions can be found. This community report is intended to give plenty of examples and inspiration for scientific discovery through integrated and accelerated ML solutions. This is followed by a high-level overview and organization of technical advances, including an abundance of pointers to source material, which can enable these breakthroughs.

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Section: Exemplars Of Domain Applicationsmentioning

confidence: 99%