FAT: An In-Memory Accelerator With Fast Addition for Ternary Weight Neural Networks
Author:
Affiliation:
1. School of Computer Science and Engineering, Nanyang Technological University, Singapore
2. HP-NTU Digital Manufacturing Corporate Laboratory, Nanyang Technological University, Singapore
Funder
Ministry of Education, Singapore, through Academic Research Fund Tier 2
Tier 1
Nanyang Technological University, Singapore, through NAP
SUG
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Computer Graphics and Computer-Aided Design,Software
Link
http://xplorestaging.ieee.org/ielx7/43/10048562/09799520.pdf?arnumber=9799520
Reference60 articles.
1. TRQ: Ternary Neural Networks With Residual Quantization
2. Modeling and Benchmarking Computing-in-Memory for Design Space Exploration
3. RTN: Reparameterized Ternary Network
4. Understanding Reuse, Performance, and Hardware Cost of DNN Dataflow
5. Eyeriss v2: A Flexible Accelerator for Emerging Deep Neural Networks on Mobile Devices
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