1,707 research outputs found
Modeling the Resource Requirements of Convolutional Neural Networks on Mobile Devices
Convolutional Neural Networks (CNNs) have revolutionized the research in
computer vision, due to their ability to capture complex patterns, resulting in
high inference accuracies. However, the increasingly complex nature of these
neural networks means that they are particularly suited for server computers
with powerful GPUs. We envision that deep learning applications will be
eventually and widely deployed on mobile devices, e.g., smartphones,
self-driving cars, and drones. Therefore, in this paper, we aim to understand
the resource requirements (time, memory) of CNNs on mobile devices. First, by
deploying several popular CNNs on mobile CPUs and GPUs, we measure and analyze
the performance and resource usage for every layer of the CNNs. Our findings
point out the potential ways of optimizing the performance on mobile devices.
Second, we model the resource requirements of the different CNN computations.
Finally, based on the measurement, pro ling, and modeling, we build and
evaluate our modeling tool, Augur, which takes a CNN configuration (descriptor)
as the input and estimates the compute time and resource usage of the CNN, to
give insights about whether and how e ciently a CNN can be run on a given
mobile platform. In doing so Augur tackles several challenges: (i) how to
overcome pro ling and measurement overhead; (ii) how to capture the variance in
different mobile platforms with different processors, memory, and cache sizes;
and (iii) how to account for the variance in the number, type and size of
layers of the different CNN configurations
An Experimental Study of Reduced-Voltage Operation in Modern FPGAs for Neural Network Acceleration
We empirically evaluate an undervolting technique, i.e., underscaling the
circuit supply voltage below the nominal level, to improve the power-efficiency
of Convolutional Neural Network (CNN) accelerators mapped to Field Programmable
Gate Arrays (FPGAs). Undervolting below a safe voltage level can lead to timing
faults due to excessive circuit latency increase. We evaluate the
reliability-power trade-off for such accelerators. Specifically, we
experimentally study the reduced-voltage operation of multiple components of
real FPGAs, characterize the corresponding reliability behavior of CNN
accelerators, propose techniques to minimize the drawbacks of reduced-voltage
operation, and combine undervolting with architectural CNN optimization
techniques, i.e., quantization and pruning. We investigate the effect of
environmental temperature on the reliability-power trade-off of such
accelerators. We perform experiments on three identical samples of modern
Xilinx ZCU102 FPGA platforms with five state-of-the-art image classification
CNN benchmarks. This approach allows us to study the effects of our
undervolting technique for both software and hardware variability. We achieve
more than 3X power-efficiency (GOPs/W) gain via undervolting. 2.6X of this gain
is the result of eliminating the voltage guardband region, i.e., the safe
voltage region below the nominal level that is set by FPGA vendor to ensure
correct functionality in worst-case environmental and circuit conditions. 43%
of the power-efficiency gain is due to further undervolting below the
guardband, which comes at the cost of accuracy loss in the CNN accelerator. We
evaluate an effective frequency underscaling technique that prevents this
accuracy loss, and find that it reduces the power-efficiency gain from 43% to
25%.Comment: To appear at the DSN 2020 conferenc
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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