EDeN: Enabling Low-Power CNN Inference on Edge Devices Using Prefetcher-assisted NVM Systems

Author:

Jang Jihoon1ORCID,Lee Hyokeun2ORCID,Kim Hyun1ORCID

Affiliation:

1. Department of Electrical and Information Engineering, Seoul National University of Science and Technology, Seoul, Seoul, Republic of Korea

2. Ajou University, Suwon, Gyeonggi-do, Republic of Korea

Publisher

ACM

Reference34 articles.

1. Real time wireless health monitoring application using mobile devices;Amna Abdullah;International Journal of Computer Networks & Communications,2015

2. Jung Ho Ahn et al. 2013. McSimA+: A manycore simulator with application-level+ simulation and detailed microarchitecture modeling. In IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS). 74--85.

3. Arm Holdings plc. 2007. Arm Cortex A9. https://developer.arm.com/Processors/Cortex-A9#Technical-Specifications

4. Jean-Loup Baer et al. 1991. An effective on-chip preloading scheme to reduce data access penalty. In ACM/IEEE Conf. Supercomputing (SC). 176--186.

5. Rahul Bera et al. 2021. Pythia: A customizable hardware prefetching framework using online reinforcement learning. In IEEE/ACM International Symposium on Microarchitecture (MICRO). 1121--1137.

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