Paper Info
Title
ZEN: A flexible energy-efficient hardware classifier exploiting temporal sparsity in ECG data
Abstract
State-of-the-art low-power ECG hardware classifiers rely on extraction of pre-defined, hand-tuned features. This hinders their usage in different applications, because of the time-consuming redesign of features for any new classification task. As an alternative, we present a machine-learning based approach to ECG classification in hardware that still relies on feature extraction but is much more flexible to use. We utilize a recurrent neural network with temporal sparsity inspired by biologically motivated event-based systems. Features are extracted by freely configurable time-domain filters that are fully integrated in the training process. These are sparsified via delta encoding, so that further processing only acts on changes in the features or the recurrent connections. A scalable hardware architecture derived from this concept allows for stand-alone classification on input data streams. Despite its flexibility, our design achieves a peak energy efficiency of 37 nJ per heartbeat and an ultra-low power consumption of 532 nW in real-time operation, driven by temporal sparsity and a systematic low-power implementation strategy. At the same time, its classification performance is on par with state-of-the-art software-based classifiers.
Year
DOI
Venue
2022
10.1109/AICAS54282.2022.9869958
2022 IEEE INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE CIRCUITS AND SYSTEMS (AICAS 2022): INTELLIGENT TECHNOLOGY IN THE POST-PANDEMIC ERA
Keywords
DocType
Citations 
Electrocardiography, Low-power electronics, Neural network hardware, Recurrent neural networks
Conference
0
PageRank 
References 
Authors
0.34
0
9
Name
Order
Citations
PageRank
Matthias Jobst100.34
Johannes Partzsch200.34
Chen Liu300.34
Liyuan Guo400.34
Dennis Walter500.34
Saif-Ur Rehman600.34
Stefan Scholze700.34
Sebastian Hoppner800.34
Christian Mayr900.34