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A Fast and Power Efficient Architecture to Parallelize LSTM based RNN for Cognitive Intelligence Applications

Published: 18 June 2017 Publication History

Abstract

Long Short-Term Memory (LSTM) based Recurrent Neural Networks (RNNs) are promising for cognitive intelligence applications like speech recognition, image caption and nature language processing, etc. However, the cascade dependent structure in RNN with huge amount of power inefficient operations like multiplication, memory accessing and nonlinear transformation, could not guarantee high computing speed and low power consumption. In this work, by exploiting semantic correlation, we propose a semantic correlation based data pre-fetch method to break the dependency and achieve parallel processing. Based on this method, a full parallel and pipeline architecture that tackles huge amount operations is designed. Experiments on benchmarks of image caption, speech recognition and language processing show that, this work improves computing speed by 5.1 times, 44.9 times and 1.53 times, respectively, and power efficiency by 1885.7 times, 4061.5 times and 127.5 times, respectively, when compared with state-of-the-art works.

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A. Graves and N. Jaitly. Towards end-to-end speech recognition with recurrent neural networks. In Proceedings of the 31st International Conference on Machine Learning (ICML-14), pages 1764--1772, 2014.
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Published In

cover image ACM Conferences
DAC '17: Proceedings of the 54th Annual Design Automation Conference 2017
June 2017
533 pages
ISBN:9781450349277
DOI:10.1145/3061639
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 18 June 2017

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  • China National High Technologies Research Program
  • China Major S&T Project

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DAC '17
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Overall Acceptance Rate 1,770 of 5,499 submissions, 32%

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  • (2023)VulHunter: Hunting Vulnerable Smart Contracts at EVM Bytecode-Level via Multiple Instance LearningIEEE Transactions on Software Engineering10.1109/TSE.2023.331720949:11(4886-4916)Online publication date: Nov-2023
  • (2023)On Hybrid Artificial Neural Networks and Variational Quantum Classifier for Network Intrusion Detection2023 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)10.1109/CyberC58899.2023.00070(410-416)Online publication date: 2-Nov-2023
  • (2023)Research progress and application of deep learning in remaining useful life, state of health and battery thermal management of lithium batteriesJournal of Energy Storage10.1016/j.est.2023.10786870(107868)Online publication date: Oct-2023
  • (2022)An ASIP for Neural Network Inference on Embedded Devices with 99% PE Utilization and 100% Memory Hidden under Low Silicon CostSensors10.3390/s2210384122:10(3841)Online publication date: 19-May-2022
  • (2022)Implementation of Bidirectional LSTM Accelerator Based on FPGA2022 IEEE 22nd International Conference on Communication Technology (ICCT)10.1109/ICCT56141.2022.10072756(1512-1516)Online publication date: 11-Nov-2022
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  • (2020)An Optimized Parallel Implementation of Non-Iteratively Trained Recurrent Neural NetworksJournal of Artificial Intelligence and Soft Computing Research10.2478/jaiscr-2021-000311:1(33-50)Online publication date: 3-Dec-2020
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