@inproceedings{76bff07fb2564328ac95a381c5d7d8e1,
title = "XNOR-POP: A processing-in-memory architecture for binary Convolutional Neural Networks in Wide-IO2 DRAMs",
abstract = "It is challenging to adopt computing-intensive and parameter-rich Convolutional Neural Networks (CNNs) in mobile devices due to limited hardware resources and low power budgets. To support multiple concurrently running applications, one mobile device needs to perform multiple CNN tests simultaneously in real-time. Previous solutions cannot guarantee a high enough frame rate when serving multiple applications with reasonable hardware and power cost. In this paper, we present a novel process-in-memory architecture to process emerging binary CNN tests in Wide-IO2 DRAMs. Compared to state-of-the-art accelerators, our design improves CNN test performance by 4× ∼ 11× with small hardware and power overhead.",
author = "Lei Jiang and Minje Kim and Wujie Wen and Danghui Wang",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 22nd IEEE/ACM International Symposium on Low Power Electronics and Design, ISLPED 2017 ; Conference date: 24-07-2017 Through 26-07-2017",
year = "2017",
month = aug,
day = "11",
doi = "10.1109/ISLPED.2017.8009163",
language = "English (US)",
series = "Proceedings of the International Symposium on Low Power Electronics and Design",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "ISLPED 2017 - IEEE/ACM International Symposium on Low Power Electronics and Design",
address = "United States",
}