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A Competitive Edge: Can FPGAs Beat GPUs at DCNN Inference Acceleration in Resource-Limited Edge Computing Applications?
[article]
2021
arXiv
pre-print
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We propose this FPGA-based accelerator to be used for Deconvolutional Neural Network (DCNN) inference in low-power edge computing applications. ...
As such, we design a spatio-temporally parallelized hardware architecture capable of accelerating a deconvolution algorithm optimized for power-efficient inference on a resource-limited FPGA. ...
We would also like to thank Parimal Patel and Stephen Neuendorffer at Xilinx and Byungheon Jeon at UC San Diego. ...
arXiv:2102.00294v2
fatcat:x6gzg7v2anhprauyrghgowlkcm
Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
2021
Journal of Big Data
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It is followed by a list of the major DL applications. Computational tools including FPGA, GPU, and CPU are summarized along with a description of their influence on DL. ...
Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cognitive tasks, matching or even beating those provided ...
and can be customized FPGA DCNN training Greater float-point capabilities provided by GPU GPU Feature
Assessment ...
doi:10.1186/s40537-021-00444-8
pmid:33816053
pmcid:PMC8010506
fatcat:x2h5qs5c2jbntipu7oi5hfnb6u
SCNN: An Accelerator for Compressed-sparse Convolutional Neural Networks
[article]
2017
arXiv
pre-print
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Our results show that on contemporary neural networks, SCNN can improve both performance and energy by a factor of 2.7x and 2.3x, respectively, over a comparably provisioned dense CNN accelerator. ...
In addition, the accumulation of multiplication products are performed in a novel accumulator array. ...
Today, training is often done on GPUs [24] or farms of GPUs, while inference depends on the application and can employ CPUs, GPUs, FPGA, or specially-built ASICs. ...
arXiv:1708.04485v1
fatcat:pt53mgyw5zh35ct3q35iopn3ba
Exploring the Design Space of Deep Convolutional Neural Networks at Large Scale
[article]
2016
arXiv
pre-print
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In recent years, the research community has discovered that deep neural networks (DNNs) and convolutional neural networks (CNNs) can yield higher accuracy than all previous solutions to a broad array of ...
Instead, the "right" CNN/DNN architecture varies depending on the application at hand. CNN/DNNs comprise an enormous design space. ...
Quantity of Communication A computational resource (e.g. one mobile phone or a cluster of servers) has a limited quantity of computation that it can perform each second. ...
arXiv:1612.06519v1
fatcat:jwo2gyfjvfh3lbkfdntctx24o4
FATNN: Fast and Accurate Ternary Neural Networks
[article]
2021
arXiv
pre-print
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To tackle these two challenges, in this work, we first show that, under some mild constraints, computational complexity of the ternary inner product can be reduced by a factor of 2. ...
Moreover, there is still a significant gap in accuracy between TNNs and full-precision networks, hampering their deployment to real applications. ...
However, a significant obstacle for deploying DCNN algorithms to mobile/embedded edge devices with limited computing resources is the ever growing computation complexity-in order to achieve good accuracy ...
arXiv:2008.05101v4
fatcat:ojcqdwbtkfeaja4iwcslyzwd7a
Exploration of Energy Efficient Hardware and Algorithms for Deep Learning
2019
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Deep Neural Networks (DNNs) have emerged as the state-of-the-art technique in a wide range of machine learning tasks for analytics and computer vision in the next generation of embedded (mobile, IoT, wearable ...
Despite their success, they suffer from high energy requirements both in inference and training. ...
The DCNNs were trained, tested and timed using NVIDIA GPUs. ...
doi:10.25394/pgs.8044442.v1
fatcat:dxvbl6ofarasta4gtpfzn535hm