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Erschienen in:
Scaling the Java Virtual Machine on a Many-Core System Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

10. Neuromorphic Hardware Acceleration Enabled by Emerging Technologies

verfasst von : Zheng Li, Chenchen Liu, Hai Li, Yiran Chen

Erschienen in: Emerging Technology and Architecture for Big-data Analytics

Verlag: Springer International Publishing

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Abstract

The explosion of big data applications imposes severe challenges of data processing speed and scalability on computing system. However, the performance of the von Neumann machine is greatly hindered by the increasing performance gap between CPU and memory, motivating the active research on new or alternative computing architectures. One important instance is the neuromorphic computing engine, which provides the capability of information processing within a compact and energy-efficient platform. Recently, many research efforts have been investigated in utilizing the latest discovered memristors array in neuromorphic systems due to the similarity of memristors to biological synapses. In this chapter, we proposed two neuromorphic system designs with feedback and feedforward methodologies, respectively. Favorable performance in terms of robustness and recognition accuracy are demonstrated by simulation results and corresponding analysis.

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Vorheriges Kapitel Adaptive Dynamic Range Compression for Improving Envelope-Based Speech Perception: Implications for Cochlear Implants
Nächstes Kapitel Energy Efficient Spiking Neural Network Design with RRAM Devices
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Metadaten
Titel
Neuromorphic Hardware Acceleration Enabled by Emerging Technologies
verfasst von
Zheng Li
Chenchen Liu
Hai Li
Yiran Chen
Copyright-Jahr
2017
Buch
Emerging Technology and Architecture for Big-data Analytics

Print ISBN: 978-3-319-54839-5

Electronic ISBN: 978-3-319-54840-1

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-54840-1_10

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