5,936 research outputs found
FastDeepIoT: Towards Understanding and Optimizing Neural Network Execution Time on Mobile and Embedded Devices
Deep neural networks show great potential as solutions to many sensing
application problems, but their excessive resource demand slows down execution
time, pausing a serious impediment to deployment on low-end devices. To address
this challenge, recent literature focused on compressing neural network size to
improve performance. We show that changing neural network size does not
proportionally affect performance attributes of interest, such as execution
time. Rather, extreme run-time nonlinearities exist over the network
configuration space. Hence, we propose a novel framework, called FastDeepIoT,
that uncovers the non-linear relation between neural network structure and
execution time, then exploits that understanding to find network configurations
that significantly improve the trade-off between execution time and accuracy on
mobile and embedded devices. FastDeepIoT makes two key contributions. First,
FastDeepIoT automatically learns an accurate and highly interpretable execution
time model for deep neural networks on the target device. This is done without
prior knowledge of either the hardware specifications or the detailed
implementation of the used deep learning library. Second, FastDeepIoT informs a
compression algorithm how to minimize execution time on the profiled device
without impacting accuracy. We evaluate FastDeepIoT using three different
sensing-related tasks on two mobile devices: Nexus 5 and Galaxy Nexus.
FastDeepIoT further reduces the neural network execution time by to
and energy consumption by to compared with the
state-of-the-art compression algorithms.Comment: Accepted by SenSys '1
EIE: Efficient Inference Engine on Compressed Deep Neural Network
State-of-the-art deep neural networks (DNNs) have hundreds of millions of
connections and are both computationally and memory intensive, making them
difficult to deploy on embedded systems with limited hardware resources and
power budgets. While custom hardware helps the computation, fetching weights
from DRAM is two orders of magnitude more expensive than ALU operations, and
dominates the required power.
Previously proposed 'Deep Compression' makes it possible to fit large DNNs
(AlexNet and VGGNet) fully in on-chip SRAM. This compression is achieved by
pruning the redundant connections and having multiple connections share the
same weight. We propose an energy efficient inference engine (EIE) that
performs inference on this compressed network model and accelerates the
resulting sparse matrix-vector multiplication with weight sharing. Going from
DRAM to SRAM gives EIE 120x energy saving; Exploiting sparsity saves 10x;
Weight sharing gives 8x; Skipping zero activations from ReLU saves another 3x.
Evaluated on nine DNN benchmarks, EIE is 189x and 13x faster when compared to
CPU and GPU implementations of the same DNN without compression. EIE has a
processing power of 102GOPS/s working directly on a compressed network,
corresponding to 3TOPS/s on an uncompressed network, and processes FC layers of
AlexNet at 1.88x10^4 frames/sec with a power dissipation of only 600mW. It is
24,000x and 3,400x more energy efficient than a CPU and GPU respectively.
Compared with DaDianNao, EIE has 2.9x, 19x and 3x better throughput, energy
efficiency and area efficiency.Comment: External Links: TheNextPlatform: http://goo.gl/f7qX0L ; O'Reilly:
https://goo.gl/Id1HNT ; Hacker News: https://goo.gl/KM72SV ; Embedded-vision:
http://goo.gl/joQNg8 ; Talk at NVIDIA GTC'16: http://goo.gl/6wJYvn ; Talk at
Embedded Vision Summit: https://goo.gl/7abFNe ; Talk at Stanford University:
https://goo.gl/6lwuer. Published as a conference paper in ISCA 201
Cloud Chaser: Real Time Deep Learning Computer Vision on Low Computing Power Devices
Internet of Things(IoT) devices, mobile phones, and robotic systems are often
denied the power of deep learning algorithms due to their limited computing
power. However, to provide time-critical services such as emergency response,
home assistance, surveillance, etc, these devices often need real-time analysis
of their camera data. This paper strives to offer a viable approach to
integrate high-performance deep learning-based computer vision algorithms with
low-resource and low-power devices by leveraging the computing power of the
cloud. By offloading the computation work to the cloud, no dedicated hardware
is needed to enable deep neural networks on existing low computing power
devices. A Raspberry Pi based robot, Cloud Chaser, is built to demonstrate the
power of using cloud computing to perform real-time vision tasks. Furthermore,
to reduce latency and improve real-time performance, compression algorithms are
proposed and evaluated for streaming real-time video frames to the cloud.Comment: Accepted to The 11th International Conference on Machine Vision (ICMV
2018). Project site: https://zhengyiluo.github.io/projects/cloudchaser
Deep Expander Networks: Efficient Deep Networks from Graph Theory
Efficient CNN designs like ResNets and DenseNet were proposed to improve
accuracy vs efficiency trade-offs. They essentially increased the connectivity,
allowing efficient information flow across layers. Inspired by these
techniques, we propose to model connections between filters of a CNN using
graphs which are simultaneously sparse and well connected. Sparsity results in
efficiency while well connectedness can preserve the expressive power of the
CNNs. We use a well-studied class of graphs from theoretical computer science
that satisfies these properties known as Expander graphs. Expander graphs are
used to model connections between filters in CNNs to design networks called
X-Nets. We present two guarantees on the connectivity of X-Nets: Each node
influences every node in a layer in logarithmic steps, and the number of paths
between two sets of nodes is proportional to the product of their sizes. We
also propose efficient training and inference algorithms, making it possible to
train deeper and wider X-Nets effectively.
Expander based models give a 4% improvement in accuracy on MobileNet over
grouped convolutions, a popular technique, which has the same sparsity but
worse connectivity. X-Nets give better performance trade-offs than the original
ResNet and DenseNet-BC architectures. We achieve model sizes comparable to
state-of-the-art pruning techniques using our simple architecture design,
without any pruning. We hope that this work motivates other approaches to
utilize results from graph theory to develop efficient network architectures.Comment: ECCV'1
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