Challenges and Trends inDeveloping Nonvolatile Memory-Enabled Computing Chips for Intelligent Edge Devices

Hung, Je-Min; Li, Xueqing; Wu, Juejian; Chang, Meng‐Fan

doi:10.1109/ted.2020.2976115

Cited by 39 publications

(24 citation statements)

References 69 publications

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“…In order to achieve informatization and standardization of physical education management in colleges and universities, it must develop a physical education management system. Through the development of the student physical education management system, the physical education management system of colleges and universities realizes management Informa ionization and standardization [24].…”

Section: Overview Of College Physical Education Managementmentioning

confidence: 99%

The Design of University Physical Education Management Framework Based on Edge Computing and Data Analysis

Sun

Gao

2021

Wireless Communications and Mobile Computing

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Educational informatization has become the only way for mankind to enter the information age, and the application of educational resource library is a key issue in the integration of information technology disciplines. Aiming at the shortcomings of existing college physical education management technology, this article is dedicated to developing a new online management platform for physical education. Firstly, the introduction of edge computing technology in the architecture design of college physical education system can fully improve the efficiency of the management system. Secondly, based on data cleaning, the BP neural network algorithm modified by particle swarm is used to conduct in-depth analysis of the data of the university sports teaching management system and obtain corresponding optimization measures. Finally, it is verified in actual tests that the system can quickly perform data search, entry, modification, deletion, and other operations, which can ensure the realization of the school sports department’s networked, scientific, standardized, and digitalized management of physical education resources.

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Section: Overview Of College Physical Education Managementmentioning

confidence: 99%

The Design of University Physical Education Management Framework Based on Edge Computing and Data Analysis

Sun

Gao

2021

Wireless Communications and Mobile Computing

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“…With the help of relevant background and language knowledge, business translators should not only have reliable language knowledge but also should have economic, legal, management, trade, and other aspects [21][22][23][24][25][26][27][28][29][30]. Then, all this knowledge will be applied to provide the background of the content involved in the translation and help the translator to complete the task better and faster.…”

Section: Business English Translation Frameworkmentioning

confidence: 99%

Research on Business English Translation Architecture Based on Artificial Intelligence Speech Recognition and Edge Computing

Xu¹

2021

Wireless Communications and Mobile Computing

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In today’s society, the continuous deepening of international cultural integration has become the background of the times. China has become more and more closely connected with the world, and many physical or online news media have become a platform for China to receive world information and spread Chinese culture. Business English translation is therefore valued by translation researchers and translators. Aiming at the shortcomings of current business English translation research, this paper designs and develops a business English translation architecture based on artificial intelligence speech recognition and edge computing. First of all, considering the relevance and complementarity between speech and text modalities, this paper uses the deep neural network feature fusion method to effectively fuse the extracted monomodal features and perform speech recognition. Secondly, adopt the edge computing method to establish the business English translation system architecture. Finally, the simulation test analysis verifies the efficiency of the business English translation framework established in this paper. Compared with the existing methods, our proposal improved the accuracy than others at least 10% and the time of model building also decreased obviously. The purpose of this research is to discuss how to deal with the many differences between the source language and the target language, and how to enhance the readability of the translation and meet the reader’s cultural cognition and needs.

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“…to implement inference on the edge due to their limited performance and power budget [4]. Luckily, most of edge device inference applications can tolerate accuracy loss to some extent, and this can be exploited for hardware trade-off to attain speed and power advantages.…”

Section: Introductionmentioning

confidence: 99%

“…Their main building blocks consist of multiply-and-add (MAC) blocks and memory. Many architectures use in-memory computing (IMC) with both traditional and emerging technologies [4]- [6].…”

Section: Introductionmentioning

confidence: 99%

Design Exploration of ReRAM-Based Crossbar for AI Inference

2021

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ReRAM-based crossbar designs utilizing mixed-signal implementation has gained importance due to their low power, small size, low cost, and high throughput especially for multiply-and-add operations in AI-related applications. This paper provides a framework with associated code for analyzing the impact of ReRAM device variation and post-training analog conductance quantization that has not been fully explored for pre-trained network accelerators. A detailed study with end-to-end implementation is presented, ranging from mapping the pre-trained DNN weights to quantized crossbar conductance values and into final classification with the presence of variation. Monte Carlo analysis was performed to better analyze the impact of the different parameters on the final accuracy without being device-specific. The work assumes different conductance value variations, different conductance dynamic ranges, and various device quantization levels (QLs). MNIST and CIFAR-10 data sets were used in this study for ANN and CNN, respectively. Results show that for simple ANN, the accuracy drop due to quantization was ∼ 2% at 64-QLs. While for CNN, the decrease in classification accuracy was around 10% with the same number of levels. Moreover, weight variation might cause a ∼ 5% and ∼ 8% drop in classification accuracy for ANN with 5% and CNN with 3% variation, respectively. The study confirms that increasing the number of levels with small variation results in nearoptimal accuracy. However, the increase in accuracy saturates at an upper limit. The amount of distortion propagated through the layers is different in the two cases. It is dependent on the complexity of input data and network structure, such as the size of the neurons in each layer, the number of layers, and the number of channels and filters at each stage. This contribution is the first to provide the framework to explore the implication that emphasizes on device-independent post-training DNN quantization and weight variation on classification accuracy. This helps explore design trade-offs especially for edge devices in cases where there is no access to the training set to the end-user due to security or cost issues for pre-trained networks. Device variation, in-memory computing, neural network, post-training, quantization, ReRAM. INDEX TERMS

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Challenges and Trends inDeveloping Nonvolatile Memory-Enabled Computing Chips for Intelligent Edge Devices

Cited by 39 publications

References 69 publications

The Design of University Physical Education Management Framework Based on Edge Computing and Data Analysis

The Design of University Physical Education Management Framework Based on Edge Computing and Data Analysis

Research on Business English Translation Architecture Based on Artificial Intelligence Speech Recognition and Edge Computing

Design Exploration of ReRAM-Based Crossbar for AI Inference

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