62,696 research outputs found
ADEPOS: Anomaly Detection based Power Saving for Predictive Maintenance using Edge Computing
In industry 4.0, predictive maintenance(PM) is one of the most important
applications pertaining to the Internet of Things(IoT). Machine learning is
used to predict the possible failure of a machine before the actual event
occurs. However, the main challenges in PM are (a) lack of enough data from
failing machines, and (b) paucity of power and bandwidth to transmit sensor
data to cloud throughout the lifetime of the machine. Alternatively, edge
computing approaches reduce data transmission and consume low energy. In this
paper, we propose Anomaly Detection based Power Saving(ADEPOS) scheme using
approximate computing through the lifetime of the machine. In the beginning of
the machines life, low accuracy computations are used when the machine is
healthy. However, on the detection of anomalies, as time progresses, the system
is switched to higher accuracy modes. We show using the NASA bearing dataset
that using ADEPOS, we need 8.8X less neurons on average and based on
post-layout results, the resultant energy savings are 6.4 to 6.65XComment: Submitted to ASP-DAC 2019, Japa
Spatio-temporal Learning with Arrays of Analog Nanosynapses
Emerging nanodevices such as resistive memories are being considered for
hardware realizations of a variety of artificial neural networks (ANNs),
including highly promising online variants of the learning approaches known as
reservoir computing (RC) and the extreme learning machine (ELM). We propose an
RC/ELM inspired learning system built with nanosynapses that performs both
on-chip projection and regression operations. To address time-dynamic tasks,
the hidden neurons of our system perform spatio-temporal integration and can be
further enhanced with variable sampling or multiple activation windows. We
detail the system and show its use in conjunction with a highly analog
nanosynapse device on a standard task with intrinsic timing dynamics- the TI-46
battery of spoken digits. The system achieves nearly perfect (99%) accuracy at
sufficient hidden layer size, which compares favorably with software results.
In addition, the model is extended to a larger dataset, the MNIST database of
handwritten digits. By translating the database into the time domain and using
variable integration windows, up to 95% classification accuracy is achieved. In
addition to an intrinsically low-power programming style, the proposed
architecture learns very quickly and can easily be converted into a spiking
system with negligible loss in performance- all features that confer
significant energy efficiency.Comment: 6 pages, 3 figures. Presented at 2017 IEEE/ACM Symposium on Nanoscale
architectures (NANOARCH
Xception: Deep Learning with Depthwise Separable Convolutions
We present an interpretation of Inception modules in convolutional neural
networks as being an intermediate step in-between regular convolution and the
depthwise separable convolution operation (a depthwise convolution followed by
a pointwise convolution). In this light, a depthwise separable convolution can
be understood as an Inception module with a maximally large number of towers.
This observation leads us to propose a novel deep convolutional neural network
architecture inspired by Inception, where Inception modules have been replaced
with depthwise separable convolutions. We show that this architecture, dubbed
Xception, slightly outperforms Inception V3 on the ImageNet dataset (which
Inception V3 was designed for), and significantly outperforms Inception V3 on a
larger image classification dataset comprising 350 million images and 17,000
classes. Since the Xception architecture has the same number of parameters as
Inception V3, the performance gains are not due to increased capacity but
rather to a more efficient use of model parameters
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