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AIgean: An Open Framework for Deploying Machine Learning on Heterogeneous Clusters / Tarafdar, Naif (Toronto U.) ; Di Guglielmo, Giuseppe (Columbia U.) ; Harris, Philip C (MIT) ; Krupa, Jeffrey D (MIT) ; Loncar, Vladimir (CERN) ; Rankin, Dylan S (MIT) ; Tran, Nhan (Fermilab) ; Wu, Zhenbin (Illinois U., Chicago) ; Shen, Qianfeng Clark (Toronto U.) ; Chow, Paul (Toronto U.)
AIgean, pronounced like the sea, is an open framework to build and deploy machine learning (ML) algorithms on a heterogeneous cluster of devices (CPUs and FPGAs). We leverage two open source projects: Galapagos, for multi-FPGA deployment, and hls4ml, for generating ML kernels synthesizable using Vivado HLS. [...]
FERMILAB-PUB-22-529-SCD.- 2022 - 32 p. - Published in : ACM Trans. Reconf. Tech. Syst. 15 (2022) 1-32

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AIgean: An Open Framework for Machine Learning on Heterogeneous Clusters / Tarafdar, Naif (Toronto U.) ; Guglielmo, Giuseppe Di (Columbia U.) ; Harris, Philip C (MIT) ; Krupa, Jeffrey D (MIT) ; Loncar, Vladimir (CERN) ; Rankin, Dylan S (MIT) ; Tran, Nhan (Fermilab) ; Wu, Zhenbin (Illinois State U.) ; Shen, Qianfeng (Toronto U.) ; Chow, Paul (Toronto U.)
Machine learning (ML) in the past decade has been one of the most popular topics of research within the computing community. Interest within the computing field ranges across all levels of the computation stack. [...]
2020 - Published in : 10.1109/fccm48280.2020.00072
In : 28th IEEE Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM 2020), Fayetteville, CA, USA, 3 - 6 May 2020, pp.239

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