Scheduling is a decision-making mechanism that allows resource sharing among several activities by determining their order of execution on the available resources. In the heterogeneous distributed systems, it is a great challenge to schedule concurrent workflows submitted by different users at different times. Scheduling with deadline and budget constraints are becoming an even more challenging issue

Efficient prototyping is an invaluable resource for modern enterprises and research centers. An efficient prototyping tool exhibits high throughput while maintaining flexibility, and reduces design and validation efforts, resulting in low time-to-market and high competitiveness. This paper presents a modular implementation of high-performance software (SW) libraries running on a Heterogeneous Computing

The goal of Industry 4.0 is to be faster, more efficient and more customer-centric, by enhancing the automation and digitalisation of production systems. Frequently, the production in Industry 4.0 is categorized as safety-critical, for example, due to the interactions between autonomous machines and hazardous substances that can result in human injury or death, damage to machines, property or the environment

At present, various types of code vulnerabilities lead to a huge workload of detection. Although multi-party cooperation detection methods can be applied to improve efficiency, common crowdsourcing models are difficult to guarantee the privacy of code and identity. We design a code and identity privacy vulnerability crowdsourcing detection system ConGradetect. The system is built on a blockchain and

Integrating idle embedded devices into cloud computing is a promising approach to support Distributed Machine Learning (DML). In this paper, we approach to address the data hiding problem in such DML systems. For the purpose of the data encryption in DML systems, we introduce the tripartite asymmetric encryption theorem to provide theoretical support. Based on the theorem, we design a general image

The legacy storage data path is largely structured in black-box layers and has four major limitations: (1) functional redundancies across layers, (2) poor cross-layer coordination and data tracking, (3) presupposition of high-latency storage devices, and (4) poor support for new storage data models. While addressing all these limitations is a daunting challenge, we introduce ADAPT, an auxiliary storage

With the explosive growth of battery-free and energy-harvesting devices, the energy harvesting powered system has gained more attentions and been widely used in different fields. However, unstable harvested energy is a challenge of energy-harvesting devices since the program execution would be interrupted frequently. Non-volatile processor (NVP) is proposed to back up volatile logics before energy

Efficient energy management and fault tolerance are two key issues that demand judicious handling while scheduling real-time task-sets. Standby-sparing technique is available in literature for fault tolerance, while dynamic power management (DPM) and dynamic voltage scaling (DVS) schemes are exploited for energy management. However, usage of auxiliary processor for running backup jobs leads to increased

Wireless Body Area Networks (WBAN) is often envisioned as a paradigm shift from the traditional healthcare system to the modern E-Healthcare system. The patient's vitals sensed by the sensors are highly sensitive, confidential, and susceptible to various attacks from adversaries. For the WBAN being a concrete application of the healthcare system, it is paramount to ensure that the data sensed by the

Developing efficient embedded vision applications requires exploring various algorithmic optimization trade-offs and a broad spectrum of hardware architecture choices. This makes navigating the solution space and finding the design points with optimal performance trade-offs a challenge for developers. To help provide a fair baseline comparison, we conducted comprehensive benchmarks of accuracy, run-time

Action recognition in video involves interpreting complex semantic information, which remains a challenge in computer vision. Research on the use of deep neural networks for video action recognition has made significant progress in recent years, but most available recognition frameworks cannot satisfactorily perform the video action recognition task in privacy scene. In light of this issue, this paper

In today’s ChipMultiprocessors (CMPs), multiple cores share the common Last Level Cache (LLC), divided into multiple banks. As the data requirement is increasing the demand for larger LLC sizes is also increasing. The traditional SRAM technology is not area efficient to design such larger LLCs as demanded by the modern CMPs. From the last few years, DRAM technologies have been used to propose LLC.

The rapid development of the Internet of Vehicles (IoVs) leads to a significant increase in the number of automobiles connected to the Internet. Therefore, to make all vehicles under a shared Internet, we need distributed systems and efficient communication design known as the vehicles cloud. When IoV transmits the monitored data to the server, it can delete, modify, or eavesdrop. Thus, this can cause

In this article, we propose Palisade, a distributed framework for streaming anomaly detection. Palisade is motivated by the need to apply multiple detection algorithms for distinct anomalies in the same scenario. Our solution blends low latency detection with deployment flexibility and ease-of-modification. This work includes a thorough description of the choices made in designing Palisade and the

Cellular networks are evolving to the era of 5th Generation (5G), where 5G new radio (NR) and Long-Term Evolution: Advanced (LTE-A) Pro technologies are being envisioned for enabling smart and innovative services of the Internet-of-Things (IoT). However, existing LTE-A Pro protocols such as the group paging approach is still heavily inclined towards human-to-human communications owing to the heterogeneous

Convolutional neural networks (CNNs) have proven to be a disruptive technology in most vision, speech and image processing tasks. Given their ubiquitous acceptance, the research community is investing a lot of time and resources on deep neural networks. Custom hardware such as ASICs are proving to be extremely worthy platforms for running such programs. However, the ever-increasing complexity of these

The vehicles in Internet of Vehicles (IoV) can be used to opportunistically gather and distribute the data in a smart city environment. However, at the same time, various security threats arise due to insecure communication happening among various entities in an IoV-based smart city deployment. To address this issue, we aim to design a novel blockchain-enabled batch authentication scheme in Artificial

To alleviate the performance degradation introduced by cryptographic operations in web-based services, dedicated hardware accelerators have emerged for calculation offloading. However, accelerators from different vendors can be endowed with variable run-time modes and optimized for different workload types. It is necessary but challenging to fully and fairly quantify the associated acceleration benefits

Heterogeneous multi-cores utilize the strength of different architectures for executing particular types of workload, and usually offer higher performance and energy efficiency. In this paper, we study a federated scheduling. Federated scheduling is a promising approach to schedule non-typed parallel real-time tasks, where each heavy task exclusively executes on a number of dedicated processors, while

In last decade, a proliferation growth in the development of computer malware has been done. Nowadays, cybercriminals (attacker) use malware as a weapon to carry out the attacks on the computer systems. Internet is the main media to execute the malware attack on the computer systems through emails, malicious websites and by drive and download software. Malicious software can be a virus, trojan horse

Intermittent computing (ImC) refers to the scenario where periods of program execution are separated by reboots. ImC systems are generally powered by energy-harvesting (EH) devices: they start executing a program when the accumulated energy reaches a threshold and stop when the energy buffer is exhausted. Since ImC does not depend on a fixed supply of power, it can be used in a wide range of scenarios/devices

Ciphertext-policy attribute-based encryption(CP-ABE) has been widely studied and used in access control schemes for secure data sharing. Since in most of the existing attribute-based encryption methods, all user attributes are managed by a single central authority, it is easy to cause a single point of failure. Therefore, several multi-authority CP-ABE schemes propose to manage user attributes by multiple

Offset-based response time analysis techniques obtain tight worst-case response time (WCRT) bounds by accounting release time dependencies between tasks. The maximum response time variation of a task or a message (i.e., the difference between the worst-case and best-case response time) is used to compute the end-to-end delays of distributed systems (Palencia and Harbour, 1998), (Tindell and Clark,

Users’ trip location information is of great value in the study of social computing and big data. Based on the personalized characteristics of user trajectory data, this paper proposes a Trajectory-based-based Identity Authentication Method (TIAM). TIAM consists of the registration phase and the authentication phase. Users initialize the trip routes and the coordinates of home and office in the registration

Transform methods, such as the Laplace and the Fourier transforms, are widely used for analyzing the continuous dynamics of the physical components of Cyber-physical Systems (CPS). Traditionally, the transform methods based analysis of CPS is conducted using paper-and-pencil proof methods, computer-based simulations or computer algebra systems. However, all these methods cannot capture the continuous

Modern multicore systems, which perform complex functionalities on densely packed multi-million gate platforms at high frequencies, are often susceptible to inappropriate surges in core temperatures, if not effectively controlled. Increasing temperature above specified limits not only leads to high cost of cooling but also results in higher dissipation of leakage power together with a reduction in

In this letter, we analyze the identity-based signature with conditional privacy-preserving authentication scheme (short for IBS-CPPA, published in Journal of Systems Architecture, doi: 10.1016/j.sysarc.2019.101692) and demonstrate that it cannot provide secure authentication between vehicles. Specifically, in IBS-CPPA, two types of adversaries can launch different attacks to forge a valid signature

Mobile edge computing (MEC) is envisioned to integrate cloud-like capabilities into the edge of networks for improving quality of service (QoS). This makes it possible for users with resource-limited devices to execute computation-intensive tasks by offloading them to MEC nodes. Extensive works have been done for MEC. However, few of them involve user mobility. Whether to migrate task dynamically can’t

For satellite communication with bi-directional channels, we investigate the application of Low-Density Parity-Check (LDPC) coding by incorporating channel state information (CSI) to enhance its security performance. Specifically, the scheme includes two main steps: CSI measurement and the design of LDPC security coding. Based on its reciprocity and uniqueness, CSI is first obtained by estimating bidirectional

Domain Wall Memory (DWM) as an ideal candidate for replacing traditional memories especially in parallel systems, has many desirable characteristics such as low leakage power, high density and low access latency. However, due to the tape-like architecture of DWM, shift operations have vital impact on performance. Considering data-intensive applications with massive loops and arrays, increasing parallelism

Recently, Wireless Sensor Network (WSN) is often viewed with an oversized range of sensors that are structured and collaborate to gather and transmit information around the targets. As sensors may be positioned in harsh surroundings, it is critical for secure data transmission. Therefore, a dynamic routing path should be essential for WSN applications. In this paper, a Jellyfish Dynamic Routing Protocol

“Recurrent neural networks” (RNNs) are powerful artificial intelligence models that have shown remarkable effectiveness in several tasks such as music generation, speech recognition and machine translation. RNN computations involve both intra-timestep and inter-timestep dependencies. Due to these features, hardware acceleration of RNNs is more challenging than that of CNNs. Recently, several researchers

Autonomous mobile robots share social spaces with humans, usually working together for domestic or professional tasks. Cyber security breaches in such robots undermine the trust between humans and robots. In this paper, we investigate how to apprehend and inflict security threats at the design and implementation stage of an autonomous mobile robot. To this end, we leverage the idea of directed fuzzing

Highly scalable resource supply capacity of cloud computing has greatly improved the execution speed of workflow applications, however, traditional workflow scheduling algorithms which focus on the optimization of makespan (execution time) of workflows, become inappropriate for the design of large-scale workflow systems. Workflow scheduling in cloud computing is particularly a multiobjective optimization

Formal methods use abstraction and rigorously verified refinement to manage the design of complex systems, ensuring that they satisfy important invariant properties. However, formal verification is not sufficient: models must also be tested to ensure that they behave according to the informal requirements and validated by domain experts who may not be expert in formal modelling. This can be satisfied

Real-time servers have been widely explored in the scheduling literature to predictably execute aperiodic activities, as well as to allow hierarchical scheduling settings. As they facilitate timing isolation between different software components, there is a renewed interest for the adoption of fixed priority real-time servers in the automotive domain, as a way to implement more efficient reservation

Traffic light detection is a key module in the autonomous driving system to enhance the interactions between drivers and unmanned vehicles. In recent studies, deep neural networks are widely used for traffic light detection and resource/power consumption is a major concern for model deployment in vehicular edge devices. This paper proposes a novel light-weight deep CNN model that integrates the multi-backbone

Over the past decade, deep convolutional neural networks (CNN) have been widely embraced in various visual recognition applications owing to their extraordinary accuracy. However, their high computational complexity and excessive data storage present two challenges when designing CNN hardware. In this paper, we propose an energy-aware bit-serial streaming deep CNN accelerator to tackle these challenges

Internet of things technology and edge computing have been applied increasingly in the field of medical treatment to solve the problem of imbalanced medical resources. To better diagnose Attention Deficit Hyperactivity Disorder (ADHD), we propose a new short-time diagnosis technology that can quickly analyze the functional magnetic resonance imaging (fMRI) of patients and assist doctors in remote diagnosis

Minimizing latency and power are key goals in the design of NoC routers. Different proposals combine lookahead routing and router bypass to skip the arbitration and buffering, reducing router delay. However, the conditions to use them requires completely empty buffers in the intermediate routers. This restricts the amount of flits that use the bypass pipeline especially at medium and high loads, increasing

Global Health sometimes faces pandemics as are currently facing COVID-19 disease. The spreading and infection factors of this disease are very high. A huge number of people from most of the countries are infected within six months from its first report of appearance and it keeps spreading. The required systems are not ready up to some stages for any pandemic; therefore, mitigation with existing capacity

This work proposes a novel approach to provide comprehensive security to IoT devices. Our approach is based on a reconfigurable hardware-based isolation and protection mechanism (IPM) that operates as a dynamic separation unit between devices and network, far from potential software manipulation. The IPM analyses communications for malicious activities and prevents damage to the IoT device. The IPM

As the most popular digital currency, Bitcoin has a high economic value, and its security has been paid more and more attention. Anomaly detection of Bitcoin has become a problem that must be solved. The existing Bitcoin anomaly detection methods only use static network models, and only the simple structural features such as node attributes and in/out-degree are considered to measure the similarities

Simultaneous localization and mapping (SLAM) becomes a solved problem in terms of the algorithms. In order to use them in autonomous mobile robots, researches are focusing on reducing computation complexity and developing embedded systems with appropriate software and hardware architecture. In this work, we aim to perform a fully embedded implementation of a SLAM on an FPGA based SoC combining an embedded

While the NAND flash based solid state drive (SSD) has been widely adopted by both custom electronics and enterprise storages, the isolation of the file system and the information of the real status of the physical storage space will inevitably degrade the overall efficiency as well as the reliability of the storage and could become the bottleneck of the high-performance test systems. Open Channel

An important application for discrete wavelet transform (DWT) is the image compression such as the JPEG2000 Standard, and GPU is a good choice for the implementation of the DWT. For the application of image compression in the space environment, this paper has discussed the fault tolerance for the forward DWT (FDWT) implemented on GPUs, to deal with the silent data corruption (SDC) errors caused by

Prefetching has been widely used in general purpose computers, especially in high performance CPUs, but has been much less used in DSPs and embedded processors. The goal of this paper is to investigate adding a perceptron, a single layer neural network [1], to straight-forward hardware prefetching techniques for embedded DSPs, and assess their performance improvement. By using industry standard benchmarks

The inter-task communication in embedded real-time systems can be achieved using various patterns and be subject to different timing constraints. One of the most basic communication patterns encountered in today’s automotive and aerospace software is the data chain. Each task of the chain reads data from the previous task and delivers the results of its computation to the next task. The data passing

In the IoT-Fog-Cloud landscape, IoT devices are connected to numerous software applications in order to fully operate. Some applications are deployed on the Fog layer, providing low-latency access to resource, whilst others are deployed on the Cloud to provide important resource capabilities and process heavy computation. In this distributed landscape, the deployment infrastructure has to adapt to

The challenges of deploying robots and autonomous vehicles call for further efforts to bridge the gap between the robotics, the real-time systems and the formal methods communities. Indeed, with robots being more and more involved in costly missions and contact with humans, a rigorous formal verification of their behavior in the presence of various real-time constraints is crucial. In order to increase

In-memory data processing frameworks (e.g., Spark) make big data analysis greatly simpler and efficient. However, stragglers that take much longer to finish than other tasks significantly degrade performance. There exist multiple factors that cause stragglers, either from the hardware resource layer or application layer, e.g. hardware heterogeneity, interference, data locality and data skew. While

This work considers exact schedulability analysis of a set of aperiodic non-preemptive tasks on a given set of computational resources, and the problem is known to be NP-Complete. We propose four different SAT-based encoding schemes to address the exact schedulability problem. Each approach requires a different number of variables and generates a distinct number of clauses. We present extensive comparative

In the current chip-multiprocessor era, 3D-stacked DRAM became an attractive alternative to mitigate the DRAM bandwidth wall problem. In a chip-multiprocessor, the 3D-stacked DRAM is architect either (a) to cache both local and remote data or (b) to cache only the local data. Caching only local data into the 3D-stacked DRAM enforces the chip-multiprocessors to suffer inter-node latency overhead while

For deep learning cloud computing platforms, file system is a fundamental and critical component. Hadoop distributed file system (HDFS) is widely used in large scale clusters due to its high performance and high availability. However, in deep learning datasets, the number of files is huge but the file size is small, making HDFS suffer a severe performance penalty. Although there have been many optimizing

This paper introduces a smart cyber physical multi-source energy system for electric vehicle applications. This system is realized in order to increase the autonomy of the vehicle as well as a good self-dispatch energy system. In this paper, three energy sources have modeled and integrated in the energy conversion chain of the vehicle. Moreover, the design of cyber energy dispatching system is proposed

Unspent Transaction Output (UTXO) set is the foundational model used in many blockchain systems to represent assets. The benefits of UTXO representation include parallel processing, privacy, etc. However, the increasing size of UTXO set is degrading the access performance and severely brings down the validation speed of blockchain further, especially in resource-constrained scenerios, such as IoT.

An AUTOSAR-based embedded software consists of a set of inter-connected Software Components (SWCs). Each SWC contains a set of runnables which are small code-fragments that should be mapped to Operating System (OS) tasks. This process of mapping runnables to tasks may affect both the system real-time schedulability and performance. This paper proposes fast and efficient algorithms for mapping runnables

Edge computing enables users to offload certain computation loads in complex applications to a nearby network consisting of multiple mobile devices such that the mobile devices’ resources can be integrated to afford these complex applications. Due to the social relationships among the owners of mobile devices, networks in edge computing are always complex and consist of multiple sub-networks intersecting

In this paper, we are interested in the energy optimization of hybrid Scratchpad Memory (SPM) which consists of SRAM and Non-Volatile Memory (NVM). We approach the energy optimization by data allocation with the consideration of the impact of program structure like branches. Our method is composed of two stages, i.e., program analysis stage and data allocation stage. In program analysis stage, we design

Wireless network on chip (WiNoC) has been proposed as a promising solution for on-chip interconnection network due to high scalability, high bandwidth, and low latency. However, the variations of traffic pattern distribution and data flow lead to congestion in wireless interfaces-equipped routers (WRs). Congestion is one of the main challenges in emerging WiNoCs that can reduce network performance