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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Vydáno v:International Conference on Field-programmable Logic and Applications s. 1 - 8
Hlavní autoři: Chen, Jeffrey, Hong, Sehwan, He, Warrick, Moon, Jinyeong, Jun, Sang-Woo
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.08.2021
Témata:
CPS
edge
iCE40
IoT
Lattices
low power
LSTM
Memory management
Neural networks
Power demand
Predictive Maintenance
Quantization (signal)
Wireless communication
Wireless sensor networks
ISSN:1946-1488
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Shrnutí: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.
ISSN:1946-1488
DOI:10.1109/FPL53798.2021.00009