@inproceedings{20c82d188e0a4e02aeb37b99157410ca,
title = "Compute-in-Time for Deep Neural Network Accelerators: Challenges and Prospects",
abstract = "Time-domain (TD) accelerators leverage both digital and analog features, thereby enabling energy-efficient computing and scaling with CMOS technology. This paper reviews state-of-the-art TD accelerators and discusses system considerations and hardware implementations, including the spatially unrolled and recursive TD architectures. Additionally, the paper analyzes the energy and area efficiency of the TD architectures for varying input resolutions and network sizes. This analysis provides insight for designers into how to choose the appropriate TD approach for a particular application.",
keywords = "Analog Domain, Recursive, Spatially Unrolled, Time-Domain (TD) Accelerators, Time-Domain Computation",
author = "{Al Maharmeh}, Hamza and Sarhan, {Nabil J.} and Hung, {Chung Chih} and Mohammed Ismail and Mohammad Alhawari",
year = "2020",
month = aug,
doi = "10.1109/MWSCAS48704.2020.9184470",
language = "English",
series = "Midwest Symposium on Circuits and Systems",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "990--993",
booktitle = "2020 IEEE 63rd International Midwest Symposium on Circuits and Systems, MWSCAS 2020 - Proceedings",
address = "美國",
note = "63rd IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2020 ; Conference date: 09-08-2020 Through 12-08-2020",
}