800 research outputs found
Deep Learning-Based Multiple Object Visual Tracking on Embedded System for IoT and Mobile Edge Computing Applications
Compute and memory demands of state-of-the-art deep learning methods are
still a shortcoming that must be addressed to make them useful at IoT
end-nodes. In particular, recent results depict a hopeful prospect for image
processing using Convolutional Neural Netwoks, CNNs, but the gap between
software and hardware implementations is already considerable for IoT and
mobile edge computing applications due to their high power consumption. This
proposal performs low-power and real time deep learning-based multiple object
visual tracking implemented on an NVIDIA Jetson TX2 development kit. It
includes a camera and wireless connection capability and it is battery powered
for mobile and outdoor applications. A collection of representative sequences
captured with the on-board camera, dETRUSC video dataset, is used to exemplify
the performance of the proposed algorithm and to facilitate benchmarking. The
results in terms of power consumption and frame rate demonstrate the
feasibility of deep learning algorithms on embedded platforms although more
effort to joint algorithm and hardware design of CNNs is needed.Comment: This work has been submitted to the IEEE for possible publication.
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An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor Analytics
Near-sensor data analytics is a promising direction for IoT endpoints, as it
minimizes energy spent on communication and reduces network load - but it also
poses security concerns, as valuable data is stored or sent over the network at
various stages of the analytics pipeline. Using encryption to protect sensitive
data at the boundary of the on-chip analytics engine is a way to address data
security issues. To cope with the combined workload of analytics and encryption
in a tight power envelope, we propose Fulmine, a System-on-Chip based on a
tightly-coupled multi-core cluster augmented with specialized blocks for
compute-intensive data processing and encryption functions, supporting software
programmability for regular computing tasks. The Fulmine SoC, fabricated in
65nm technology, consumes less than 20mW on average at 0.8V achieving an
efficiency of up to 70pJ/B in encryption, 50pJ/px in convolution, or up to
25MIPS/mW in software. As a strong argument for real-life flexible application
of our platform, we show experimental results for three secure analytics use
cases: secure autonomous aerial surveillance with a state-of-the-art deep CNN
consuming 3.16pJ per equivalent RISC op; local CNN-based face detection with
secured remote recognition in 5.74pJ/op; and seizure detection with encrypted
data collection from EEG within 12.7pJ/op.Comment: 15 pages, 12 figures, accepted for publication to the IEEE
Transactions on Circuits and Systems - I: Regular Paper
Memory and information processing in neuromorphic systems
A striking difference between brain-inspired neuromorphic processors and
current von Neumann processors architectures is the way in which memory and
processing is organized. As Information and Communication Technologies continue
to address the need for increased computational power through the increase of
cores within a digital processor, neuromorphic engineers and scientists can
complement this need by building processor architectures where memory is
distributed with the processing. In this paper we present a survey of
brain-inspired processor architectures that support models of cortical networks
and deep neural networks. These architectures range from serial clocked
implementations of multi-neuron systems to massively parallel asynchronous ones
and from purely digital systems to mixed analog/digital systems which implement
more biological-like models of neurons and synapses together with a suite of
adaptation and learning mechanisms analogous to the ones found in biological
nervous systems. We describe the advantages of the different approaches being
pursued and present the challenges that need to be addressed for building
artificial neural processing systems that can display the richness of behaviors
seen in biological systems.Comment: Submitted to Proceedings of IEEE, review of recently proposed
neuromorphic computing platforms and system
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