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Similarity-Based LSTM Architecture for Energy-Efficient Edge-Level Speech Recognition
- Similarity-Based LSTM Architecture for Energy-Efficient Edge-Level Speech Recognition
- Jo, Junseo; Kung, Jaeha; Lee, Sunggu; Lee, Youngjoo
- DGIST Authors
- Kung, Jaeha
- Issue Date
- IEEE International Symposium on Low-Power Electronics and Design
- Targeting the resource-limited edge devices, we present a novel processing architecture of long short-term memory (LSTM) networks for low-power speech recognition. The proposed scheme newly defines the similarity score between two inputs of adjacent LSTM cells, and then the processing mode of the current LSTM cell is dynamically determined to reduce the energy while providing the accurate recognition. If the similarity is high, more precisely, the current cell is disabled and the outputs are directly copied from the prior vectors, totally eliminating complex LSTM operations. To maximize the skipping ratio without degrading the accuracy, for the first time, we analyze the effects of skipping the consecutive cells and set the upper limit of the number of consecutive skips. When two adjacent inputs are weakly similar, in addition, we modify the concept of the previous delta-computing, which approximately activate the LSTM cell with low computational resolution, further reducing the energy consumption. Compared to the previous state-of-the-art solutions, as a result, the proposed LSTM architecture remarkably saves the energy consumed for the accurate speech recognition, which is suitable to the resource-limited embedded edges. © 2019 IEEE.
- Institute of Electrical and Electronics Engineers Inc.
- Related Researcher
Intelligent Digital Systems Lab
딥러닝, 가속하드웨어, 저전력 하드웨어, 고성능 시스템
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- Department of Information and Communication EngineeringIntelligent Digital Systems Lab2. Conference Papers
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