Analysis of Conventional, Near-Memory, and In-Memory DNN Accelerators-Reference-Cited by-同舟云学术

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Analysis of Conventional, Near-Memory, and In-Memory DNN Accelerators

Published:2023-04 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)
language:
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Author:

Glint Tom¹,Jha Chandan Kumar²,Awasthi Manu³,Mekie Joycee¹

Affiliation:

1. IIT Gandhinagar,India

2. DFKI,Germany

3. Ashoka University,India

Funder

Science and Engineering Research Board

Semiconductor Research Corporation

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10158063/10158070/10158207.pdf?arnumber=10158207

Reference13 articles.

1. A 1ynm 1.25V 8Gb, 16Gb/s/pin GDDR6-based Accelerator-in-Memory supporting 1TFLOPS MAC Operation and Various Activation Functions for Deep-Learning Applications

2. Newton: A DRAM-maker’s Accelerator-in-Memory (AiM) Architecture for Machine Learning

3. Hydra: A near hybrid memory accelerator for CNN inference

4. Benchmarking a new paradigm: An experimental analysis of a real processing-in-memory architecture;gomez-luna;arXiv preprint arXiv 2105 03814,2021

5. Timeloop: A Systematic Approach to DNN Accelerator Evaluation

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Design of Hardware Accelerator Architecture for Target Detection Based on ZYNQ;2023 3rd International Conference on Electronic Information Engineering and Computer Science (EIECS);2023-09-22

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