Eciton: Very Low-Power LSTM Neural Network Accelerator for Predictive Maintenance at the Edge
This paper presents Eciton, a very low-power LSTM neural network accelerator for low-power edge sensor nodes, demonstrating real-time processing on predictive maintenance applications with a power consumption of 17 mW under load. Eciton reduces memory and chip resource requirements via 8-bit quantiz...
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| Published in: | International Conference on Field-programmable Logic and Applications pp. 1 - 8 |
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| Main Authors: | , , , , |
| Format: | Conference Proceeding |
| Language: | English |
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01.08.2021
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| ISSN: | 1946-1488 |
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| Abstract | This paper presents Eciton, a very low-power LSTM neural network accelerator for low-power edge sensor nodes, demonstrating real-time processing on predictive maintenance applications with a power consumption of 17 mW under load. Eciton reduces memory and chip resource requirements via 8-bit quantization and hard sigmoid activation, allowing the accelerator as well as the LSTM model parameters to fit in a lowcost, low-power Lattice iCE40 UP5K FPGA. Eciton demonstrates real-time processing at a very low power consumption with minimal loss of accuracy on two predictive maintenance scenarios with differing characteristics, while achieving competitive power efficiency against the state-of-the-art of similar scale. We also show that the addition of this accelerator actually reduces the power budget of the sensor node by reducing power-hungry wireless transmission.The resulting power budget of the sensor node is small enough to be powered by a power harvester, potentially allowing it to run indefinitely without a battery or periodic maintenance. |
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| AbstractList | This paper presents Eciton, a very low-power LSTM neural network accelerator for low-power edge sensor nodes, demonstrating real-time processing on predictive maintenance applications with a power consumption of 17 mW under load. Eciton reduces memory and chip resource requirements via 8-bit quantization and hard sigmoid activation, allowing the accelerator as well as the LSTM model parameters to fit in a lowcost, low-power Lattice iCE40 UP5K FPGA. Eciton demonstrates real-time processing at a very low power consumption with minimal loss of accuracy on two predictive maintenance scenarios with differing characteristics, while achieving competitive power efficiency against the state-of-the-art of similar scale. We also show that the addition of this accelerator actually reduces the power budget of the sensor node by reducing power-hungry wireless transmission.The resulting power budget of the sensor node is small enough to be powered by a power harvester, potentially allowing it to run indefinitely without a battery or periodic maintenance. |
| Author | Chen, Jeffrey He, Warrick Moon, Jinyeong Hong, Sehwan Jun, Sang-Woo |
| Author_xml | – sequence: 1 givenname: Jeffrey surname: Chen fullname: Chen, Jeffrey email: jeffrc2@uci.edu organization: University of California,Department of Computer Science,Irvine – sequence: 2 givenname: Sehwan surname: Hong fullname: Hong, Sehwan email: sehwanh@uci.edu organization: University of California,Department of Computer Science,Irvine – sequence: 3 givenname: Warrick surname: He fullname: He, Warrick email: warrickhe@gmail.com organization: Diamond Bar High School – sequence: 4 givenname: Jinyeong surname: Moon fullname: Moon, Jinyeong email: j.moon@fsu.edu organization: Florida State University,Department of Electrical and Computer Engineering – sequence: 5 givenname: Sang-Woo surname: Jun fullname: Jun, Sang-Woo email: swjun@uci.edu organization: University of California,Department of Computer Science,Irvine |
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| Snippet | This paper presents Eciton, a very low-power LSTM neural network accelerator for low-power edge sensor nodes, demonstrating real-time processing on predictive... |
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| SubjectTerms | CPS edge iCE40 IoT Lattices low power LSTM Memory management Neural networks Power demand Predictive Maintenance Quantization (signal) Wireless communication Wireless sensor networks |
| Title | Eciton: Very Low-Power LSTM Neural Network Accelerator for Predictive Maintenance at the Edge |
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