scholarly journals Designing Automated Deployment Strategies of Face Recognition Solutions in Heterogeneous IoT Platforms

Information ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 532
Author(s):  
Unai Elordi ◽  
Chiara Lunerti ◽  
Luis Unzueta ◽  
Jon Goenetxea ◽  
Nerea Aranjuelo ◽  
...  
Keyword(s):  
Neural Networks ◽  
Face Recognition ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
User Interaction ◽  
Biometric Data ◽  
Optimal Deployment ◽  
Iot Platforms ◽  
Iot Devices ◽  
Hardware Platforms

In this paper, we tackle the problem of deploying face recognition (FR) solutions in heterogeneous Internet of Things (IoT) platforms. The main challenges are the optimal deployment of deep neural networks (DNNs) in the high variety of IoT devices (e.g., robots, tablets, smartphones, etc.), the secure management of biometric data while respecting the users’ privacy, and the design of appropriate user interaction with facial verification mechanisms for all kinds of users. We analyze different approaches to solving all these challenges and propose a knowledge-driven methodology for the automated deployment of DNN-based FR solutions in IoT devices, with the secure management of biometric data, and real-time feedback for improved interaction. We provide some practical examples and experimental results with state-of-the-art DNNs for FR in Intel’s and NVIDIA’s hardware platforms as IoT devices.

scholarly journals Memory-Based Pruning of Deep Neural Networks for IoT Devices Applied to Flood Detection

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7506
Author(s):  
Francisco Erivaldo Fernandes Junior ◽  
Luis Gustavo Nonato ◽  
Caetano Mazzoni Ranieri ◽  
Jó Ueyama
Keyword(s):  
Neural Networks ◽  
Control Systems ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Damage Control ◽  
Memory Footprint ◽  
Flood Detection ◽  
Iot Devices ◽  
Internet Connections ◽  
Flooding Events

Automatic flood detection may be an important component for triggering damage control systems and minimizing the risk of social or economic impacts caused by flooding. Riverside images from regular cameras are a widely available resource that can be used for tackling this problem. Nevertheless, state-of-the-art neural networks, the most suitable approach for this type of computer vision task, are usually resource-consuming, which poses a challenge for deploying these models within low-capability Internet of Things (IoT) devices with unstable internet connections. In this work, we propose a deep neural network (DNN) architecture pruning algorithm capable of finding a pruned version of a given DNN within a user-specified memory footprint. Our results demonstrate that our proposed algorithm can find a pruned DNN model with the specified memory footprint with little to no degradation of its segmentation performance. Finally, we show that our algorithm can be used in a memory-constraint wireless sensor network (WSN) employed to detect flooding events of urban rivers, and the resulting pruned models have competitive results compared with the original models.

scholarly journals An efficient pruning scheme of deep neural networks for Internet of Things applications

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Chen Qi ◽  
Shibo Shen ◽  
Rongpeng Li ◽  
Zhifeng Zhao ◽  
Qing Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Internet Of Things ◽  
Deep Neural Networks ◽  
Computational Cost ◽  
Superior Performance ◽  
Compact Structure ◽  
Resource Limited ◽  
Benchmark Datasets ◽  
Iot Devices

AbstractNowadays, deep neural networks (DNNs) have been rapidly deployed to realize a number of functionalities like sensing, imaging, classification, recognition, etc. However, the computational-intensive requirement of DNNs makes it difficult to be applicable for resource-limited Internet of Things (IoT) devices. In this paper, we propose a novel pruning-based paradigm that aims to reduce the computational cost of DNNs, by uncovering a more compact structure and learning the effective weights therein, on the basis of not compromising the expressive capability of DNNs. In particular, our algorithm can achieve efficient end-to-end training that transfers a redundant neural network to a compact one with a specifically targeted compression rate directly. We comprehensively evaluate our approach on various representative benchmark datasets and compared with typical advanced convolutional neural network (CNN) architectures. The experimental results verify the superior performance and robust effectiveness of our scheme. For example, when pruning VGG on CIFAR-10, our proposed scheme is able to significantly reduce its FLOPs (floating-point operations) and number of parameters with a proportion of 76.2% and 94.1%, respectively, while still maintaining a satisfactory accuracy. To sum up, our scheme could facilitate the integration of DNNs into the common machine-learning-based IoT framework and establish distributed training of neural networks in both cloud and edge.

scholarly journals Representing Deep Neural Networks Latent Space Geometries with Graphs

Algorithms ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 39
Author(s):  
Carlos Lassance ◽  
Vincent Gripon ◽  
Antonio Ortega
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Objective Function ◽  
Learning Process ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
The Core ◽  
Learning Tasks ◽  
Latent Space

Deep Learning (DL) has attracted a lot of attention for its ability to reach state-of-the-art performance in many machine learning tasks. The core principle of DL methods consists of training composite architectures in an end-to-end fashion, where inputs are associated with outputs trained to optimize an objective function. Because of their compositional nature, DL architectures naturally exhibit several intermediate representations of the inputs, which belong to so-called latent spaces. When treated individually, these intermediate representations are most of the time unconstrained during the learning process, as it is unclear which properties should be favored. However, when processing a batch of inputs concurrently, the corresponding set of intermediate representations exhibit relations (what we call a geometry) on which desired properties can be sought. In this work, we show that it is possible to introduce constraints on these latent geometries to address various problems. In more detail, we propose to represent geometries by constructing similarity graphs from the intermediate representations obtained when processing a batch of inputs. By constraining these Latent Geometry Graphs (LGGs), we address the three following problems: (i) reproducing the behavior of a teacher architecture is achieved by mimicking its geometry, (ii) designing efficient embeddings for classification is achieved by targeting specific geometries, and (iii) robustness to deviations on inputs is achieved via enforcing smooth variation of geometry between consecutive latent spaces. Using standard vision benchmarks, we demonstrate the ability of the proposed geometry-based methods in solving the considered problems.

scholarly journals Reconfigurable Binary Neural Network Accelerator with Adaptive Parallelism Scheme

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 230
Author(s):  
Jaechan Cho ◽  
Yongchul Jung ◽  
Seongjoo Lee ◽  
Yunho Jung
Keyword(s):  
Neural Networks ◽  
High Throughput ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Throughput Performance ◽  
Adaptive Parallelism ◽  
Sensor Applications ◽  
Binary Neural Network ◽  
Target Layer ◽  
Network Topologies

Binary neural networks (BNNs) have attracted significant interest for the implementation of deep neural networks (DNNs) on resource-constrained edge devices, and various BNN accelerator architectures have been proposed to achieve higher efficiency. BNN accelerators can be divided into two categories: streaming and layer accelerators. Although streaming accelerators designed for a specific BNN network topology provide high throughput, they are infeasible for various sensor applications in edge AI because of their complexity and inflexibility. In contrast, layer accelerators with reasonable resources can support various network topologies, but they operate with the same parallelism for all the layers of the BNN, which degrades throughput performance at certain layers. To overcome this problem, we propose a BNN accelerator with adaptive parallelism that offers high throughput performance in all layers. The proposed accelerator analyzes target layer parameters and operates with optimal parallelism using reasonable resources. In addition, this architecture is able to fully compute all types of BNN layers thanks to its reconfigurability, and it can achieve a higher area–speed efficiency than existing accelerators. In performance evaluation using state-of-the-art BNN topologies, the designed BNN accelerator achieved an area–speed efficiency 9.69 times higher than previous FPGA implementations and 24% higher than existing VLSI implementations for BNNs.

Deep Neural Networks for Face Recognition: Pairwise Optimisation

2018 ◽  
pp. 103-110
Author(s):  
Elitsa Popova ◽  
Athanasios Athanasopoulos ◽  
Efraim Ie ◽  
Nikolaos Christou ◽  
Ndifreke Nyah
Keyword(s):  
Neural Networks ◽  
Face Recognition ◽  
Deep Neural Networks

scholarly journals Label Distribution for Learning with Noisy Labels

2020 ◽  
Author(s):  
Yun-Peng Liu ◽  
Ning Xu ◽  
Yu Zhang ◽  
Xin Geng
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Confidence Estimation ◽  
Novel Method ◽  
Real World Datasets ◽  
Label Distribution ◽  
Noisy Labels

The performances of deep neural networks (DNNs) crucially rely on the quality of labeling. In some situations, labels are easily corrupted, and therefore some labels become noisy labels. Thus, designing algorithms that deal with noisy labels is of great importance for learning robust DNNs. However, it is difficult to distinguish between clean labels and noisy labels, which becomes the bottleneck of many methods. To address the problem, this paper proposes a novel method named Label Distribution based Confidence Estimation (LDCE). LDCE estimates the confidence of the observed labels based on label distribution. Then, the boundary between clean labels and noisy labels becomes clear according to confidence scores. To verify the effectiveness of the method, LDCE is combined with the existing learning algorithm to train robust DNNs. Experiments on both synthetic and real-world datasets substantiate the superiority of the proposed algorithm against state-of-the-art methods.

Towards Evaluating the Reliability of Deep Neural Networks based IoT Devices

2021 ◽  
pp. 1-1
Author(s):  
Mingyuan Fan ◽  
Yang Liu ◽  
Cen Chen ◽  
Shengxing Yu ◽  
Wenzhong Guo ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Iot Devices

Framework for TCAD augmented machine learning on multi- I–V characteristics using convolutional neural network and multiprocessing

2021 ◽  
Vol 42 (12) ◽  
pp. 124101
Author(s):  
Thomas Hirtz ◽  
Steyn Huurman ◽  
He Tian ◽  
Yi Yang ◽  
Tian-Ling Ren
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Information Technologies ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Data Driven ◽  
Sufficient Data ◽  
Learning Models ◽  
Simulation Tools ◽  
New Information

Abstract In a world where data is increasingly important for making breakthroughs, microelectronics is a field where data is sparse and hard to acquire. Only a few entities have the infrastructure that is required to automate the fabrication and testing of semiconductor devices. This infrastructure is crucial for generating sufficient data for the use of new information technologies. This situation generates a cleavage between most of the researchers and the industry. To address this issue, this paper will introduce a widely applicable approach for creating custom datasets using simulation tools and parallel computing. The multi-I–V curves that we obtained were processed simultaneously using convolutional neural networks, which gave us the ability to predict a full set of device characteristics with a single inference. We prove the potential of this approach through two concrete examples of useful deep learning models that were trained using the generated data. We believe that this work can act as a bridge between the state-of-the-art of data-driven methods and more classical semiconductor research, such as device engineering, yield engineering or process monitoring. Moreover, this research gives the opportunity to anybody to start experimenting with deep neural networks and machine learning in the field of microelectronics, without the need for expensive experimentation infrastructure.

scholarly journals Detecting Emotions in English and Arabic Tweets

Information ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 98 ◽  
Author(s):  
Tariq Ahmad ◽  
Allan Ramsay ◽  
Hanady Ahmed
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Learning Algorithms ◽  
General Purpose ◽  
Machine Learning Algorithms ◽  
Current State ◽  
Optimal Thresholds ◽  
Alternative Approach

Assigning sentiment labels to documents is, at first sight, a standard multi-label classification task. Many approaches have been used for this task, but the current state-of-the-art solutions use deep neural networks (DNNs). As such, it seems likely that standard machine learning algorithms, such as these, will provide an effective approach. We describe an alternative approach, involving the use of probabilities to construct a weighted lexicon of sentiment terms, then modifying the lexicon and calculating optimal thresholds for each class. We show that this approach outperforms the use of DNNs and other standard algorithms. We believe that DNNs are not a universal panacea and that paying attention to the nature of the data that you are trying to learn from can be more important than trying out ever more powerful general purpose machine learning algorithms.

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