Quantization Friendly MobileNet (QF-MobileNet) Architecture for Vision Based Applications on Embedded Platforms.

Convolutional neural networks (CNN) have achieved excellent results in the field of image recognition that classifies objects in images. A typical CNN consists of a deep architecture that uses a large number of weights and layers to achieve high performance. CNN requires relatively large memory space and computational costs, which not only increase the time to train the model but also limit the real-time application of the trained model. For this reason, various neural network compression methodologies have been studied to efficiently use CNN in small embedded hardware such as mobile and edge devices. In this paper, we propose a kernel density estimation based non-uniform quantization methodology that can perform compression efficiently. The proposed method performs efficient weights quantization using a significantly smaller number of sampled weights than the number of original weights. Four-bit quantization experiments on the classification of the ImageNet dataset with various CNN architectures show that the proposed methodology can perform weights quantization efficiently in terms of computational costs without significant reduction in model performance.

Efficient Weights Quantization of Convolutional Neural Networks Using Kernel Density Estimation based Non-uniform Quantizer

Deep neural networks(DNN) are being extensively used in the field of image recognition, natural language processing(NLP), bioinformatics, computer vision domains and many other fields. DNN applications require huge models as they are computationally expensive, over-parameterized and have high memory footprints which becomes a bottleneck in deployment on embedded devices. Optimization of DNN models is crucial. In this paper, we have summarized the recent works done on optimization of DNNs using various pruning techniques and their comparisons. Pruning technique aims at eliminating less salient connections, neurons, channels, filters, thereby making DNNs model easier to deploy on embedded devices.

Survey on Comparative Study of Pruning Mechanism on MobileNetV3 Model

Increasing crossover of information and wireless network technologies in the medical field embarks a major revolution. Wire body area network (WBAN) is one such result of crossover. Low-cost, lightweight, multipurpose sensors can be easily integrated into a wireless communication network for health monitoring. It is a wireless networking technology based on radio frequency, consisting of small sensors, transmitting the data which could be further used for medical or safeguarding measures, and thus, there is a need to introduce better safety measures in WBAN schemes. In this paper, we proposed a novel authentication scheme for WBAN with anonymity and provided a formal security proof through BAN logic.

A Novel Authentication Scheme for Wireless Body Area Networks with Anonymity

—Object SLAM uses additional semantic informa- tion to detect and map objects in the scene, in order to improve the system’s perception and map representation capabilities. Quadrics and cubes are often used to represent objects, but their single shape limits the accuracy of object map and thus affects the application of downstream tasks. In this paper, we introduce superquadrics (SQ) with shape parameters into SLAM for representing objects, and propose a separate parameter estimation method that can accurately estimate object pose and adapt to different shapes. Furthermore, we present a lightweight data association strategy for correctly associating semantic observations in multiple views with object landmarks. We implement a monocular semantic SLAM system with real- time performance and conduct comprehensive experiments on public datasets. The results show that our method is able to build accurate object map and has advantages in object representation. Code will be released upon acceptance.

SQ-SLAM: Monocular Semantic SLAM Based on Superquadric Object Representation

Loop closure is necessary for correcting errors accumulated in simultaneous localization and mapping (SLAM) in unknown environments. However, conventional loop closure methods based on low-level geometric or image features may cause high ambiguity by not distinguishing similar scenarios. Thus, incorrect loop closures can occur. Though semantic 2D image information is considered in some literature to detect loop closures, there is little work that compares 3D scenes as an integral part of a semantic SLAM system. This letter introduces an approach, called SmSLAM+LCD, integrated into a semantic SLAM system to combine high-level 3D semantic information and low-level feature information to conduct accurate loop closure detection and effective drift reduction. The effectiveness of our approach is demonstrated in testing results.

Towards Accurate Loop Closure Detection in Semantic SLAM With 3D Semantic Covisibility Graphs

This paper addresses a challenging problem - how to reduce energy consumption without incurring performance drop when deploying deep neural networks (DNNs) at the inference stage. In order to alleviate the computation and storage burdens, we propose a novel dataflow-based joint quantization approach with the hypothesis that a fewer number of quantization operations would incur less information loss and thus improve the final performance. It first introduces a quantization scheme with efficient bit-shifting and rounding operations to represent network parameters and activations in low precision. Then it restructures the network architectures to form unified modules for optimization on the quantized model. Extensive experiments on ImageNet and KITTI validate the effectiveness of our model, demonstrating that state-of-the-art results for various tasks can be achieved by this quantized model. Besides, we designed and synthesized an RTL model to measure the hardware costs among various quantization methods. For each quantization operation, it reduces area cost by about 15 times and energy consumption by about 9 times, compared to a strong baseline.

Jie Fu

Papers

Towards Accurate Loop Closure Detection in Semantic SLAM With 3D Semantic Covisibility Graphs

Dataflow-based Joint Quantization of Weights and Activations for Deep Neural Networks