Paper Info
Title
Accurate Neuron Resilience Prediction For A Flexible Reliability Management In Neural Network Accelerators
Abstract
Deep neural networks have become a ubiquitous tool for mastering complex classification tasks. Current research focuses on the development of power-efficient and fast neural network hardware accelerators for mobile and embedded devices. However, when used in safety-critical applications, for example autonomously operating vehicles, the reliability of such accelerators becomes a further optimization criterion which can stand in contrast to power-efficiency and latency. Furthermore, ensuring hardware reliability becomes increasingly challenging for shrinking structure widths and rising power densities in the nanometer semiconductor technology era. One solution to this challenge is the exploitation of fault tolerant parts in deep neural networks. In this paper we propose a new method for predicting the error resilience of neurons in deep neural networks and show that this method significantly improves upon existing methods in terms of accuracy as well as interpretability. We evaluate prediction accuracy by simulating hardware faults in networks trained on the CIFAR-10 and ILSVRC image classification benchmarks and protecting neurons according to the resilience estimations. In addition, we demonstrate how our resilience prediction can be used for a flexible trade-off between reliability and efficiency in neural network hardware accelerators.
Year
Venue
Field
2018
PROCEEDINGS OF THE 2018 DESIGN, AUTOMATION & TEST IN EUROPE CONFERENCE & EXHIBITION (DATE)
Psychological resilience,Interpretability,Semiconductor technology,Latency (engineering),Computer science,Real-time computing,Fault tolerance,Contextual image classification,Artificial neural network,Reliability management,Distributed computing
DocType
ISSN
Citations 
Conference
1530-1591
2
PageRank 
References 
Authors
0.35
0
3
Name
Order
Citations
PageRank
Christoph Schorn152.74
Andre Guntoro22011.05
Gerd Ascheid31205144.76