@inproceedings{26afa5256aff47de9cafeeb3188d43f3,
title = "An energy efficient in-memory computing machine learning classifier scheme",
abstract = "Large-scale machine learning (ML) algorithms require extensive memory interactions. Managing or preventing data movement can significantly increase the speed and efficiency of many ML tasks. Towards this end, we devise an energy efficient in-memory computing kernel for a ML linear classifier and a prototype is designed. Compared with another in-memory computing kernel for ML applications [1], we achieve a power savings of over 6.4 times than a conventional discrete system while improving reliability by 54.67\%. We employ a split-data-aware technique to manage process, voltage and temperature variations. We utilize a trimodal architecture with hierarchical tree structure to further decrease power consumption. Our scheme provides a fast, energy efficient, and competitively accurate binary classification kernel.",
keywords = "Classifier, Hybrid, In-memory computing, Low power, Machine Learning, Trimodal",
author = "Shixiong Jiang and Priya, \{Sheena Ratnam\} and Naveena Elango and James Clay and Ramalingam Sridhar",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 32nd International Conference on VLSI Design, VLSID 2019 ; Conference date: 05-01-2019 Through 09-01-2019",
year = "2019",
month = may,
day = "9",
doi = "10.1109/VLSID.2019.00046",
language = "English",
series = "Proceedings - 32nd International Conference on VLSI Design, VLSID 2019 - Held concurrently with 18th International Conference on Embedded Systems, ES 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "157--162",
booktitle = "Proceedings - 32nd International Conference on VLSI Design, VLSID 2019 - Held concurrently with 18th International Conference on Embedded Systems, ES 2019",
address = "United States",
}