@inproceedings{1edcd801c15c4d06814101bd875bdaf7,
title = "Deep Mapper: A Multi-Channel Single-Cycle Near-Sensor DNN Accelerator",
abstract = "This work proposes the Deep Mapper, as a new near-sensor resistive accelerator architecture for Deep Neural Networks (DNN) inference that co-integrates the sensing and computing phases of resource-constrained edge devices. Deep Mapper is developed to intrinsically realize highly parallelized multi-channel processing of input frames supported by a new dense hardware-friendly mapping methodology. Our circuit-to-application simulation results on the DNN acceleration task show that Deep Mapper reaches an efficiency of 4.71 TOp/s/W outperforming state-of-the-art near-/in-sensor accelerators.",
author = "Mehrdad Morsali and Sepehr Tabrizchi and Maximilian Liehr and Nathaniel Cady and Mohsen Imani and Arman Roohi and Shaahin Angizi",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 8th IEEE International Conference on Rebooting Computing, ICRC 2023 ; Conference date: 05-12-2023 Through 06-12-2023",
year = "2023",
doi = "10.1109/ICRC60800.2023.10386958",
language = "English (US)",
series = "2023 IEEE International Conference on Rebooting Computing, ICRC 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2023 IEEE International Conference on Rebooting Computing, ICRC 2023",
address = "United States",
}