Accelerator Design for Convolutional Neural Network with Vertical Data Streaming

2018 ◽  
Author(s):  
Shanliao Li ◽  
Ouyang Ning ◽  
Zheng Wang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Data Streaming ◽  
Vertical Data ◽  
Accelerator Design

scholarly journals A Survey on System-Level Design of Neural Network Accelerators

2021 ◽  
Vol 16 (2) ◽  
pp. 1-10
Author(s):  
Kenshu Seto
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
System Level ◽  
System Level Design ◽  
Future Directions ◽  
Recent Trends ◽  
Memory Optimizations ◽  
Loop Optimizations ◽  
Accelerator Design ◽  
Nested Loop

In this paper, we present a brief survey on the system-level optimizations used for convolutional neural network (CNN) inference accelerators. For the nested loop of convolutional (CONV) layers, we discuss the effects of loop optimizations such as loop interchange, tiling, unrolling and fusion on CNN accelerators. We also explain memory optimizations that are effective with the loop optimizations. In addition, we discuss streaming architectures and single computation engine architectures that are commonly used in CNN accelerators. Optimizations for CNN models are briefly explained, followed by the recent trends and future directions of the CNN accelerator design.

scholarly journals Rapid prediction of earthquake ground shaking intensity using raw waveform data and a convolutional neural network

2020 ◽  
Vol 222 (2) ◽  
pp. 1379-1389
Author(s):  
Dario Jozinović ◽  
Anthony Lomax ◽  
Ivan Štajduhar ◽  
Alberto Michelini
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Central Italy ◽  
Error Variance ◽  
Data Streaming ◽  
Time Data ◽  
Origin Time ◽  
Waveform Data ◽  
Ground Shaking ◽  
Magnitude Comparison

SUMMARY This study describes a deep convolutional neural network (CNN) based technique to predict intensity measurements (IMs) of earthquake ground shaking. The input data to the CNN model consists of multistation, 3C acceleration waveforms recorded during the 2016 Central Italy earthquake sequence for M ≥ 3.0 events. Using a 10 s window starting at the earthquake origin time, we find that the CNN is capable of accurately predicting IMs at stations far from the epicentre which have not yet recorded the maximum ground shaking. The CNN IM predictions do not require previous knowledge of the earthquake source (location and magnitude). Comparison between the CNN model predictions and those obtained with the Bindi et al. GMPE (which requires location and magnitude) shows that the CNN model features similar error variance but smaller bias. Although the technique is not strictly designed for earthquake early warning, we find that it can provide useful estimates of ground motions within 15–20 s after earthquake origin time depending on various setup elements (e.g. times for data transmission, computation, latencies). The technique has been tested on raw data without any initial data pre-selection in order to closely replicate real-time data streaming. When noise examples were included with the earthquake data the CNN was found to be stable, accurately predicting the ground shaking intensity corresponding to the noise amplitude.

CONVOLUTIONAL NEURAL NETWORK BASED ALGORITHM FOR CECUM ACHIEVEMENT CONFIRMATION

2020 ◽  
Author(s):  
S Kashin ◽  
D Zavyalov ◽  
A Rusakov ◽  
V Khryashchev ◽  
A Lebedev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network

Non-Blind Image Deconvolution Based on “Ringing” Removal Using Convolutional Neural Network

2020 ◽  
Vol 2020 (10) ◽  
pp. 181-1-181-7
Author(s):  
Takahiro Kudo ◽  
Takanori Fujisawa ◽  
Takuro Yamaguchi ◽  
Masaaki Ikehara
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Large Scale ◽  
Blind Deconvolution ◽  
Training Dataset ◽  
Image Deconvolution ◽  
Classic Problem ◽  
Key Points ◽  
Blind Image

Image deconvolution has been an important issue recently. It has two kinds of approaches: non-blind and blind. Non-blind deconvolution is a classic problem of image deblurring, which assumes that the PSF is known and does not change universally in space. Recently, Convolutional Neural Network (CNN) has been used for non-blind deconvolution. Though CNNs can deal with complex changes for unknown images, some CNN-based conventional methods can only handle small PSFs and does not consider the use of large PSFs in the real world. In this paper we propose a non-blind deconvolution framework based on a CNN that can remove large scale ringing in a deblurred image. Our method has three key points. The first is that our network architecture is able to preserve both large and small features in the image. The second is that the training dataset is created to preserve the details. The third is that we extend the images to minimize the effects of large ringing on the image borders. In our experiments, we used three kinds of large PSFs and were able to observe high-precision results from our method both quantitatively and qualitatively.

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