Kernel Shape Control for Row-Efficient Convolution on Processing-In-Memory Arrays-Reference-Cited by-同舟云学术

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Kernel Shape Control for Row-Efficient Convolution on Processing-In-Memory Arrays

Published:2023-10-28 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD)
language:
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Author:

Rhe Johnny¹,Jeon Kang Eun¹,Lee Joo Chan²,Jeong Seongmoon²,Ko Jong Hwan³

Affiliation:

1. Sungkyunkwan University,Department of Electrical and Computer Engineering,Suwon,South Korea

2. Sungkyunkwan University,Department of Artificial Intelligence,Suwon,South Korea

3. College of Information and Communication Engineering, Sungkyunkwan University,Suwon,South Korea

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10323590/10323543/10323749.pdf?arnumber=10323749

Reference30 articles.

1. High PE Utilization CNN Accelerator with Channel Fusion Supporting Pattern-Compressed Sparse Neural Networks

2. High Area/Energy Efficiency RRAM CNN Accelerator with Pattern-Pruning-Based Weight Mapping Scheme

3. GraphR: Accelerating Graph Processing Using ReRAM

4. A scalable processing-in-memory accelerator for parallel graph processing

5. Exploring Compute-in-Memory Architecture Granularity for Structured Pruning of Neural Networks

Cited by 3 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. Performance Analysis of 3D Stacked Memory Architectures in High Performance Computing;2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE);2024-05-14

2. ConvMapSim: Modeling and Simulating Convolutional Weight Mapping for PIM Arrays;2024 IEEE 6th International Conference on AI Circuits and Systems (AICAS);2024-04-22

3. KERNTROL: Kernel Shape Control Toward Ultimate Memory Utilization for In-Memory Convolutional Weight Mapping;IEEE Transactions on Circuits and Systems I: Regular Papers;2024

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