Performance Benchmarking of Spin-Orbit Torque Magnetic RAM (SOT-MRAM) for Deep Neural Network (DNN) Accelerators
Author:
Affiliation:
1. School of Electrical and Computer Engineering, Georgia Institute of Technology,Atlanta,GA,USA,30332
2. Stanford University,Department of Materials Science and Engineering,Stanford,CA,USA,94305
Funder
SRC
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9779263/9779243/09779279.pdf?arnumber=9779279
Reference21 articles.
1. Monolithically Integrated RRAM- and CMOS-Based In-Memory Computing Optimizations for Efficient Deep Learning
2. A FeFET based super-low-power ultra-fast embedded NVM technology for 22nm FDSOI and beyond
3. Timeloop: A Systematic Approach to DNN Accelerator Evaluation
4. A Ferroelectric-based Volatile/Non-volatile Dual-mode Buffer Memory for Deep Neural Network Accelerators
5. Highly Efficient Spin-Current Generation by the Spin Hall Effect in Au1−xPtx
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. On-Device Continual Learning With STT-Assisted-SOT MRAM-Based In-Memory Computing;IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems;2024-08
2. High-speed emerging memories for AI hardware accelerators;Nature Reviews Electrical Engineering;2024-01-11
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