Lookupx: Next-Generation Quantization and Lookup Techniques for Empowering Performance and Energy Efficiency

Cagla Irmak Rumelili Koksal*, Nihat Mert Cicek, Ayse Yilmazer Metin, Berna Ors

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Long Short Term Memory (LSTM) networks as one of the most used Recurrent Neural Networks (RNN) structures offer high accuracy for sequence learning tasks. However, it is challenging to offer low latency and high throughput while satisfying the low power constraints at the same time for computationally expensive LSTM operations. This work offers a two-pronged approach to accelerate inference in RNN networks. First, linear quantization technique is applied to reduce the complexity of operations, power consumption and required memory resources. Then, a new activation implementation method is proposed, called lookupx, to accelerate sigmoid function computation during inference. It is shown that lowering precision to 4-bit integer numbers for inputs causes only 2% accuracy loss and the lookupx activation methodology has 1.9x better performance and 50x lower power consumption while decreasing the required chip area 1.2x compared to integer domain activation functions with the same accuracy result.

Original languageEnglish
Title of host publicationICECS 2023 - 2023 30th IEEE International Conference on Electronics, Circuits and Systems
Subtitle of host publicationTechnosapiens for Saving Humanity
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350326499
DOIs
Publication statusPublished - 2023
Event30th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2023 - Istanbul, Turkey
Duration: 4 Dec 20237 Dec 2023

Publication series

NameICECS 2023 - 2023 30th IEEE International Conference on Electronics, Circuits and Systems: Technosapiens for Saving Humanity

Conference

Conference30th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2023
Country/TerritoryTurkey
CityIstanbul
Period4/12/237/12/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

  • accelerator
  • low power
  • LSTM
  • nonlinear activation functions
  • quantization
  • RNN

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