Stable Matching Scheduler for Single-ISA Heterogeneous Multi-core Processors

Wang, Lei; Liu, Shaoli; Lu, Chao; Zhang, Longbing; Xiao, Junhua; Wang, Jian

doi:10.1007/978-3-319-23216-4_4

Cited by 4 publications

(1 citation statement)

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“…The hardware area of the neuron is estimated as about 1.347 mm 2 based on a 45 nm CMOS technology [ 35 ]. It can also be calculated from Reference [ 36 ] that the total hardware area of the ANN is >0.798 mm 2 .…”

Section: Methodsmentioning

confidence: 99%

Case Study—Spiking Neural Network Hardware System for Structural Health Monitoring

Pang

Liu

Harkin

et al. 2020

Sensors

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This case study provides feasibility analysis of adapting Spiking Neural Networks (SNN) based Structural Health Monitoring (SHM) system to explore low-cost solution for inspection of structural health of damaged buildings which survived after natural disaster that is, earthquakes or similar activities. Various techniques are used to detect the structural health status of a building for performance benchmarking, including different feature extraction methods and classification techniques (e.g., SNN, K-means and artificial neural network etc.). The SNN is utilized to process the sensory data generated from full-scale seven-story reinforced concrete building to verify the classification performances. Results show that the proposed SNN hardware has high classification accuracy, reliability, longevity and low hardware area overhead.

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Section: Methodsmentioning

confidence: 99%