NDLI: A self-healing autonomous neural network hardware for trustworthy biomedical systems

Content Provider	IEEE Xplore Digital Library
Author	Zhanpeng Jin Cheng, A.C.
Copyright Year	2011
Description	Author affiliation: Architecture & Systems, Nokia Research Center, Berkeley, CA 94704 (Cheng, A.C.) \|\| Department of Electrical and Computer Engineering, Binghamton University, State University of New York, Binghamton, NY 13902-6000 (Zhanpeng Jin)
Abstract	Artificial Neural Networks (ANN) have proven to be effective in solving various emerging biomedical applications through specialized ANN hardware. Unfortunately, these ANN-based biomedical systems are increasingly vulnerable to both transient and permanent faults, potentially imposing serious threats to human well-being. Inspired by the self-healing and self-recovery mechanisms of the human nervous system, this paper seeks to address reliability issues of ANN-based hardware by proposing an Autonomously Reconfigurable Artificial Neural Network (ARANN) architectural framework capable of adapting its network structures and operations, both algorithmically and microarchitecturally, to react to unexpected errors. Specifically, we propose three key techniques — Distributed ANN, Neuron Virtualization, and Dual-Layer Checkpointing — to achieve cost-effective structural adaptations and facilitate accurate system recovery. Prototyped and demonstrated on a Virtex-5 FPGA, ARANN can cover and adapt 93% chip area (neurons) with less than 1% chip overhead and O(n) reconfiguration latency.
Starting Page	1
Ending Page	8
File Size	819897
Page Count	8
File Format	PDF
ISBN	9781457717413
e-ISBN	9781457717406
DOI	10.1109/FPT.2011.6132669
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-12
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neurons Artificial neural networks Biological neural networks Synchronization Hardware Training Computer architecture
Content Type	Text
Resource Type	Article

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