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Hardware Compilation of Deep Neural Networks: An Overview

Ruizhe Zhao, Shuanglong Liu, Ho-Cheung Ng, Erwei Wang, James J. Davis, Xinyu Niu, Xiwei Wang, Huifeng Shi, George A. Constantinides, Peter Y. K. Cheung, Wayne Luk
2018 2018 IEEE 29th International Conference on Application-specific Systems, Architectures and Processors (ASAP)  
Deploying a deep neural network model on a reconfigurable platform, such as an FPGA, is challenging due to the enormous design spaces of both network models and hardware design.  ...  This paper provides an overview of recent literature proposing novel approaches to achieve this aim.  ...  INTRODUCTION Deep neural networks (DNNs) represent one of the most effective classes of machine learning techniques, and a range of DNNs have been applied in application domains including image classification  ... 
doi:10.1109/asap.2018.8445088 dblp:conf/asap/ZhaoLNWDNWSCCL18 fatcat:v5txrrsfifa6bah2oksjdlrsgi

An Overview of Efficient Interconnection Networks for Deep Neural Network Accelerators

Seyed Morteza Nabavinejad, Mohammad Baharloo, Kun-Chih Chen, Maurizio Palesi, Tim Kogel, Masoumeh Ebrahimi
2020 IEEE Journal on Emerging and Selected Topics in Circuits and Systems  
Deep Neural Networks (DNNs) have shown significant advantages in many domains, such as pattern recognition, prediction, and control optimization.  ...  First, we provide an overview of the different interconnection methods on the DNN accelerator. Then, the interconnection methods on the non-ASIC DNN accelerator will be discussed.  ...  Therefore, this article aims to provide an overview of different interconnection methods on DNN operations according to different design scenarios.  ... 
doi:10.1109/jetcas.2020.3022920 fatcat:idqitgwnrnegbd4dhrly3xsxbi

Deep Learning on Image Denoising: An overview [article]

Chunwei Tian, Lunke Fei, Wenxian Zheng, Yong Xu, Wangmeng Zuo, Chia-Wen Lin
2020 arXiv   pre-print
We first classify the deep convolutional neural networks (CNNs) for additive white noisy images; the deep CNNs for real noisy images; the deep CNNs for blind denoising and the deep CNNs for hybrid noisy  ...  Then, we analyze the motivations and principles of the different types of deep learning methods.  ...  Table 1 provides an overview of CNNs for AWNI denoising.  ... 
arXiv:1912.13171v4 fatcat:4ts2xpivhreptelbgeqhljjiri

Advances In Malware Detection- An Overview [article]

Heena
2021 arXiv   pre-print
comparatively good among all detection techniques like signature based, deep learning based, mobile/IOT and cloud based detection but still it is not able to detect all zero day malware which shows the  ...  This paper describes a literature review of various methods of malware detection.  ...  Berman [14] gives the detailed view of various neural networks used in malware detection like deep belief network, Recurrent NN, Convolutional NN,Generative adversarial network, Recursive NN and various  ... 
arXiv:2104.01835v2 fatcat:5epa552isbexxlavkav63op5ui

Quantum Computing: An Overview Across the System Stack [article]

Salonik Resch, Ulya R. Karpuzcu
2019 arXiv   pre-print
Much effort is being applied at all levels of the system stack, from the creation of quantum algorithms to the development of hardware devices.  ...  As a result, quantum computing has become one of the hottest areas of research in the last few years.  ...  Deep neural networks (DNNs) are of particular interest due to the incredible success of deep learning in classical neural networks.  ... 
arXiv:1905.07240v3 fatcat:ro3kgt6nfvd6fppdo4moisxwfm

An Overview of Vehicular Cybersecurity for Intelligent Connected Vehicles

Tian Guan, Yi Han, Nan Kang, Ningye Tang, Xu Chen, Shu Wang
2022 Sustainability  
The necessity of vehicle network security research and deployment is also analyzed.  ...  Then we analyze three common methods of abnormal intrusion detection in vehicle networks and explore the future research for preventing attacks on the network security of intelligent vehicle systems.  ...  [59], deep neural network [78], Bayesian network [60], rnn-lstm [46]  ... 
doi:10.3390/su14095211 fatcat:vwjvbpeauvfihahquk25eaxjh4

AN OVERVIEW OF DEEP LEARNING TECHNIQUES FOR SHORT-TERM ELECTRICITY LOAD FORECASTING

Saheed ADEWUYI, Segun AINA, Moses UZUNUIGBE, Aderonke LAWAL, Adeniran OLUWARANTI
2019 Applied Computer Science  
This paper presents an overview of some Deep Learning (DL) techniques applicable to forecasting electricity consumptions, especially in the short-term horizon.  ...  The eventual implication of the study is to present an insightful direction about concepts of the DL methods for forecasting electricity loads in the short-term period, especially to a potential researcher  ...  Kuo and Huang (2018) , studied an introduction of accurate deep neural network algorithm for short-term load forecasting (STLF).  ... 
doi:10.23743/acs-2019-31 doaj:cc760fc6b0a44ce292b3770441ec3b59 fatcat:xvs6k2a4xvh3nbx3fs2fohczsa

An Empirical Overview of the No Free Lunch Theorem and Its Effect on Real-World Machine Learning Classification

David Gómez, Alfonso Rojas
2016 Neural Computation  
A sizable amount of research has been done to improve the mechanisms for knowledge extraction such as machine learning classification or regression.  ...  This letter explores empirically the effect of the NFL theorem on some popular machine learning classification techniques over real-world data sets.  ...  Figure 8 : 8 Results of deep neural networks on the multiple features data set.  ... 
doi:10.1162/neco_a_00793 pmid:26599713 fatcat:75svc23aozhzdc6g67h2fb5kaq

Current Industry 4.0 Platforms – An Overview

Sauer Christian, Eichelberger Holger, Ahmadian Amir Shayan, Dewes Andreas, Jürjens Jan
2021 Zenodo  
This white paper provides an overview of current Industry 4.0 platforms, particularly from the perspective of the IIP-Ecosphere project, which is funded by the German Federal Ministry for Economic Affairs  ...  The document describes the approach to data collection, the detailed results for individual industrial platforms, and a summarizing overview.  ...  A soft Neural Processing Unit (NPU), based on a high-performance field-programmable gate array (FPGA), accelerates deep neural network (DNN) inferencing, with applications in computer vision and natural  ... 
doi:10.5281/zenodo.4485756 fatcat:f4m2swkalnh3biy5vjypsix32y

Using Graph Neural Networks to model the performance of Deep Neural Networks [article]

Shikhar Singh, Benoit Steiner, James Hegarty, Hugh Leather
2021 arXiv   pre-print
Such models speed up the compilation process by obviating the need to benchmark an enormous number of candidate implementations, referred to as schedules, on hardware.  ...  Existing performance models employ feed-forward networks, recurrent networks, or decision tree ensembles to estimate the performance of different implementations of a neural network.  ...  Index Terms-Deep Learning, Neural Networks, Code Optimization, Performance Modeling, Graph Neural Networks. I.  ... 
arXiv:2108.12489v1 fatcat:xvzxtg33uvhjzij4v2qrbnbfra

Chameleon: Adaptive Code Optimization for Expedited Deep Neural Network Compilation [article]

Byung Hoon Ahn, Prannoy Pilligundla, Amir Yazdanbakhsh, Hadi Esmaeilzadeh
2020 arXiv   pre-print
Experimentation with real hardware shows that Chameleon provides 4.45x speed up in optimization time over AutoTVM, while also improving inference time of the modern deep networks by 5.6%.  ...  Achieving faster execution with shorter compilation time can foster further diversity and innovation in neural networks.  ...  IN DEEP NEURAL NETWORK COMPILATION The general life-cycle of deep learning models from its birth to deployment comprises of two major stages.  ... 
arXiv:2001.08743v1 fatcat:we2zi5q3mvc4hezbcou2kvrleu

Machine Learning Based Indoor Localization Using Wi-Fi RSSI Fingerprints: An Overview

Navneet Singh, Sangho Choe, Rajiv Punmiya
2021 IEEE Access  
INDEX TERMS Machine learning, fingerprints, indoor localization, positioning, deep learning, received signal strength indicator, Wi-Fi.  ...  In the era of the Internet of Things (IoT) and Industry 4.0, the indoor usage of smart devices is expected to increase, thereby making their location information more important.  ...  An autoencoder is a type of neural network comprising two components: an encoder and a decoder.  ... 
doi:10.1109/access.2021.3111083 fatcat:7o6zb7kycrgftfpsukuwnsl24m

POETS: A Parallel Cluster Architecture for Spiking Neural Network

Mahyar Shahsavari, Department of Electrical and Electronic Engineering, Imperial College London, UK, Jonathan Beaumont, David Thomas, Andrew D. Brown
2021 International Journal of Machine Learning and Computing  
Spiking Neural Networks (SNNs) are known as a branch of neuromorphic computing and are currently used in neuroscience applications to understand and model the biological brain.  ...  This work presents a highly-scalable hardware platform called POETS, and uses it to implement SNN on a very large number of parallel and reconfigurable FPGA-based processors.  ...  An overview of a neuron section to be represented as a graph and read by compiler as an xml format file. Fig. 3 . 3 Fig. 3.  ... 
doi:10.18178/ijmlc.2021.11.4.1048 fatcat:znyzi4g735dudj4o2lxibcwppe

Internet of Things Based Intelligent Techniques in Workable Computing: An Overview

Jiayi Guo, Shah Nazir, Zhongguo Yang
2021 Scientific Programming  
The network of IoT is generally interconnected with different devices through the Internet.  ...  The study has considered the search process in the most popular libraries and presented an analysis of the research work done so far.  ...  For this purpose, a novel IoT edge model based on data flow and distributed deep neural network (DF-DDNN) was proposed for big data environments. e methodology has caused a latency lessening of up to 33%  ... 
doi:10.1155/2021/6805104 fatcat:o7vhob2ihneuffg5qrxhjv3ace

Tuna: A Static Analysis Approach to Optimizing Deep Neural Networks [article]

Yao Wang, Xingyu Zhou, Yanming Wang, Rui Li, Yong Wu, Vin Sharma
2021 arXiv   pre-print
The optimization of tensor operations such as convolutions and matrix multiplications is the key to improving the performance of deep neural networks.  ...  We introduce Tuna, a static analysis approach to optimizing deep neural network programs.  ...  TABLE III : III Entire network compilation cost of Tuna VS AutoTVM. of both Tuna and AutoTVM to optimize neural networks.  ... 
arXiv:2104.14641v3 fatcat:yea2apeyjbf65i22omcioejoky
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