Reconfigurable Processing-in-Memory Architecture for Data Intensive Applications
Author:
Affiliation:
1. George Mason University,Department of Electrical and Computer Engineering,Fairfax,VA,USA
2. Rochester Institute of Technology,Department of Computer Engineering,Rochester,USA
Funder
National Science Foundation
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10483275/10483296/10483311.pdf?arnumber=10483311
Reference23 articles.
1. The history began from alexnet: A comprehensive survey on deep learning approaches;Alom;arXiv,2018
2. MRIMA: An MRAM-Based In-Memory Accelerator
3. A Survey on Machine Learning Accelerators and Evolutionary Hardware Platforms
4. Heterogeneous Multi-Functional Look-Up-Table-based Processing-in-Memory Architecture for Deep Learning Acceleration
5. POLAR: Performance-aware On-device Learning Capable Programmable Processing-in-Memory Architecture for Low-Power ML Applications
Cited by 2 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. ReApprox-PIM: Reconfigurable Approximate Lookup-Table (LUT)-Based Processing-in-Memory (PIM) Machine Learning Accelerator;IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems;2024-08
2. A Comprehensive Review of Processing-in-Memory Architectures for Deep Neural Networks;Computers;2024-07-16
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