We present PULP-NN, an optimized computing library for a parallel ultra-low-power tightly coupled cluster of RISC-V processors. The key innovation in PULP-NN is a set of kernels for quantized neural ...network inference, targeting byte and sub-byte data types, down to INT-1, tuned for the recent trend toward aggressive quantization in deep neural network inference. The proposed library exploits both the digital signal processing extensions available in the PULP RISC-V processors and the cluster's parallelism, achieving up to 15.5 MACs/cycle on INT-8 and improving performance by up to 63 × with respect to a sequential implementation on a single RISC-V core implementing the baseline RV32IMC ISA. Using PULP-NN, a CIFAR-10 network on an octa-core cluster runs in 30 × and 19.6 × less clock cycles than the current state-of-the-art ARM CMSIS-NN library, running on STM32L4 and STM32H7 MCUs, respectively. The proposed library, when running on a GAP-8 processor, outperforms by 36.8 × and by 7.45 × the execution on energy efficient MCUs such as STM32L4 and high-end MCUs such as STM32H7 respectively, when operating at the maximum frequency. The energy efficiency on GAP-8 is 14.1 × higher than STM32L4 and 39.5 × higher than STM32H7, at the maximum efficiency operating point. This article is part of the theme issue 'Harmonizing energy-autonomous computing and intelligence'.
Intelligent traffic systems for traffic surveillance and monitoring have become a topic of great interest to some cities in the world. Generally, the existing traffic surveillance systems are made up ...of costly equipment with complicated operational procedures and have difficulties with congestion, occlusion, and lighting night/day and day/night transitions. In this paper, we propose an embedded system for traffic surveillance that can be utilized under these challenging conditions. This system analyses traffic and particularly focuses on the problem of detecting and categorizing traffic objects in several traffic scenarios. Moreover, it contains a robust detector produced by an original specialization framework. The proposed specialization framework utilizes a generic deep detector so as to improve the detection accuracy in a specific traffic scenario. The experiments demonstrate that the proposed specialization framework presents encouraging results for multi-traffic object detection and outperforms the state-of-the-art specialization frameworks on several public traffic datasets.
Continuous deployment has been practiced for many years by companies developing web‐ and cloud‐based applications. To succeed with continuous deployment, these companies have a strong collaboration ...culture between the operations and development teams. In addition, these companies use AI, analytics, and big data to assist with time‐consuming postdeployment activities such as continuous monitoring and fault identification. Thus, the term AIOps has evolved to highlight the importance and difficulty of maintaining highly available applications in a complex and dynamic environment. In contrast, software‐intensive embedded systems often provide customer product‐related services, such as maintenance, optimization, and support. These services are critical for these companies as they provide significant revenue and increase customer satisfaction. Therefore, the objective of our study is to gain an in‐depth understanding of the impact of continuous deployment on product‐related services provided by software‐intensive embedded systems companies. In addition, we aim to understand how AIOps can support continuous deployment in the context of software‐intensive embedded systems. To address this objective, we conducted a case study at a large and multinational telecommunications systems provider focusing on the radio access network (RAN) systems for 4G and 5G networks. The company provides RAN products and three complementing services: rollout, optimization, and customer support. The results from the case study show that the boundaries between product‐related services become blurry with continuous deployment. In addition, product‐related services, which were conducted in sequence by independent projects, converge with continuous deployment and become part of the same project. Further, AIOps platforms play an important role in reducing costs and increasing postdeployment activities' efficiency and speed. These results show that continuous deployment has a profound impact on the software‐intensive system's provider service organization. The service organization becomes the connection between the R&D organization and the customer. In order to cope with the increased speed of releases, deployment and postdeployment activities need to be largely automated. AIOps platforms are seen as a critical enabler in managing the increasing complexity without increasing human involvement.
With continuous deployment, the complexity of software‐intensive embedded systems increases. Therefore, the efforts needed to support and service these systems increase. AIOps can be a critical enabler in managing the increasing complexity without increasing human effort.
We present an implementation of the self-energy embedding theory (SEET) for periodic systems and provide a fully self-consistent embedding solution for a simple realistic periodic ...problemone-dimensional (1D) crystalline hydrogenthat displays many of the features present in complex real materials. For this system, we observe a remarkable agreement between our finite-temperature periodic implementation results and well-established and accurate zero-temperature auxiliary quantum Monte Carlo data extrapolated to thermodynamic limit. We discuss differences and similarities with other Green’s function embedding methods and provide the detailed algorithmic steps crucial for highly accurate and reproducible results.
A Survey on Network Embedding Cui, Peng; Wang, Xiao; Pei, Jian ...
IEEE transactions on knowledge and data engineering,
05/2019, Volume:
31, Issue:
5
Journal Article
Peer reviewed
Open access
Network embedding assigns nodes in a network to low-dimensional representations and effectively preserves the network structure. Recently, a significant amount of progresses have been made toward ...this emerging network analysis paradigm. In this survey, we focus on categorizing and then reviewing the current development on network embedding methods, and point out its future research directions. We first summarize the motivation of network embedding. We discuss the classical graph embedding algorithms and their relationship with network embedding. Afterwards and primarily, we provide a comprehensive overview of a large number of network embedding methods in a systematic manner, covering the structure- and property-preserving network embedding methods, the network embedding methods with side information, and the advanced information preserving network embedding methods. Moreover, several evaluation approaches for network embedding and some useful online resources, including the network data sets and softwares, are reviewed, too. Finally, we discuss the framework of exploiting these network embedding methods to build an effective system and point out some potential future directions.
In many working and recreational activities, there are scenarios where both individual and collective safety have to be constantly checked and properly signaled, as occurring in dangerous workplaces ...or during pandemic events like the recent COVID-19 disease. From wearing personal protective equipment to filling physical spaces with an adequate number of people, it is clear that a possibly automatic solution would help to check compliance with the established rules. Based on an off-the-shelf compact and low-cost hardware, we present a deployed real use-case embedded system capable of perceiving people’s behavior and aggregations and supervising the appliance of a set of rules relying on a configurable plug-in framework. Working on indoor and outdoor environments, we show that our implementation of counting people aggregations, measuring their reciprocal physical distances, and checking the proper usage of protective equipment is an effective yet open framework for monitoring human activities in critical conditions.
•Monitoring compliance with safety rules is crucial in critical environments.•Presenting a Computer Vision-based AI-assisted system to monitor human activities.•Modular architecture for pedestrian detection, counting, distancing, and PPE detection.•Two novel datasets of PPE detection and overall system evaluation in a real scenario.•State-of-the-art trained models in a deployed real use-case scenario in Pisa, Italy.
Energy presents a crucial subject of study for researchers in the Internet of Things (IoT) and especially in the Wireless Sensors Networks (WSNs). Our work is interested in the technology of IEEE ...802.15.4. Indeed, the latter proves to display several noticeable spe- cificities and characteristics qualifying it, displaying two different operating modes, na- mely the Beacon enabled mode and the non-Beacon enabled one. In the present work, a multi-threshold approach is proposed. It consists of adjusting the remaining energy in the battery of the sensor node whenever the designated energy threshold is reached. To control the activity level of the sensor node via reducing the energy consumption, the Superframe Duration SD and the Beacon Interval BI are adjusted. Many thresholds were activated to prove the efficiency of our proposed methodology. The different simulation results were compared to four other methods.
PDF
