DNA-TEQ: An Adaptive Exponential Quantization of Tensors for DNN Inference
Author:
Affiliation:
1. Universitat Politècnica de Catalunya (UPC),dept. of Computer Architecture,Barcelona,Spain
Funder
Spanish State Research Agency (MCIN/AEI)
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10487008/10487042/10487066.pdf?arnumber=10487066
Reference33 articles.
1. Language models are few-shot learners;Brown;Advances in neural information processing systems (NIPS),2020
2. A Deep Look into Logarithmic Quantization of Model Parameters in Neural Networks
3. A survey of model compression and acceleration for deep neural networks;Cheng;arXiv,2017
4. Low-precision logarithmic arithmetic for neural network accelerators
5. PermDNN: Efficient Compressed DNN Architecture with Permuted Diagonal Matrices
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