VPU-CIM: A 130nm, 33.98 TOPS/W RRAM based Compute-In-Memory Vector Co-Processor
Author:
Affiliation:
1. Indian Institute of Technology Delhi,New Delhi,India
2. International Institute of Information Technology,Bangalore,India
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10557746/10557828/10558155.pdf?arnumber=10558155
Reference15 articles.
1. XNOR-RRAM: A scalable and parallel resistive synaptic architecture for binary neural networks
2. 15.4 A 22nm 2Mb ReRAM Compute-in-Memory Macro with 121-28TOPS/W for Multibit MAC Computing for Tiny AI Edge Devices
3. Fully Binarized, Parallel, RRAM-Based Computing Primitive for In-Memory Similarity Search
4. A 40-nm, 64-Kb, 56.67 TOPS/W Voltage-Sensing Computing-In-Memory/Digital RRAM Macro Supporting Iterative Write With Verification and Online Read-Disturb Detection
5. Dual-configuration in-memory computing bitcells using SiOx RRAM for binary neural networks
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