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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Bibliographic Details

Published in:	International Conference on Field-programmable Logic and Applications pp. 1 - 8
Main Authors:	Chen, Jeffrey, Hong, Sehwan, He, Warrick, Moon, Jinyeong, Jun, Sang-Woo
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.08.2021
Subjects:	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
Online Access:	Get full text
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