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Activation Density based Mixed-Precision Quantization for Energy Efficient Neural Networks

  • Published:2021-02-01 Issue: Volume: Page:
  • ISSN:
  • Container-title:2021 Design, Automation & Test in Europe Conference & Exhibition (DATE)
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Author:

Vasquez Karina,Venkatesha Yeshwanth,Bhattacharjee Abhiroop,Moitra Abhishek,Panda Priyadarshini

Funder

National Science Foundation, Amazon Research Award, and Technology Innovation Institute Abu Dhabi

Publisher

IEEE

Cited by 12 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. Benchmarking Emerging Deep Learning Quantization Methods for Energy Efficiency;2024 IEEE 21st International Conference on Software Architecture Companion (ICSA-C);2024-06-04

2. Reconfigurable Processing-in-Memory Architecture for Data Intensive Applications;2024 37th International Conference on VLSI Design and 2024 23rd International Conference on Embedded Systems (VLSID);2024-01-06

3. Mixed Precision Based Parallel Optimization of Tensor Mathematical Operations on a New-generation Sunway Processor;2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing (CCGrid);2023-05

4. Coarse-Grained High-speed Reconfigurable Array-based Approximate Accelerator for Deep Learning Applications;2023 57th Annual Conference on Information Sciences and Systems (CISS);2023-03-22

5. Impact of Mixed Precision Techniques on Training and Inference Efficiency of Deep Neural Networks;IEEE Access;2023
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