Related papers: Reconfiguration Strategies for Online Hardware Mul…

In today's typical industrial environments, the computation of the data distribution schedules is highly centralised. Typically, a central entity configures the data forwarding paths so as to guarantee low delivery delays between data…

In recent years, Convolutional Neural Networks (ConvNets) have become an enabling technology for a wide range of novel embedded Artificial Intelligence systems. Across the range of applications, the performance needs vary significantly,…

We present a new online algorithm for profit-oriented scheduling on multiple speed-scalable processors. Moreover, we provide a tight analysis of the algorithm's competitiveness. Our results generalize and improve upon work by…

Hardware data prefetcher engines have been extensively used to reduce the impact of memory latency. However, microprocessors' hardware prefetcher engines do not include any automatic hardware control able to dynamically tune their…

Distributed quantum computing (DQC) is being actively investigated as a means of scaling the number of qubits across multiple connected quantum devices. This includes quantum circuit compilation and execution management on multiple quantum…

One of the key advantages of Software-Defined Networks (SDN) is the opportunity to integrate traffic engineering modules able to optimize network configuration according to traffic. Ideally, network should be dynamically reconfigured as…

Modern embedded computing platforms consist of a high amount of heterogeneous resources, which allows executing multiple applications on a single device. The number of running application on the system varies with time and so does the…

Wireless Powered Mobile Edge Computing (WP-MEC) integrates mobile edge computing (MEC) with wireless power transfer (WPT) to simultaneously extend the operational lifetime and enhance the computational capability of wireless devices (WDs).…

Computing elements of CPSs must be flexible to ensure interoperability; and adaptive to cope with the evolving internal and external state, such as battery level and critical tasks. Cryptography is a common task needed in CPSs to guarantee…

Integer linear programs of configurations, or configuration IPs, are a classical tool in the design of algorithms for scheduling and packing problems, where a set of items has to be placed in multiple target locations. Herein a…

Modern networked systems are increasingly reconfigurable, enabling demand-aware infrastructures whose resources can be adjusted according to the workload they currently serve. Such dynamic adjustments can be exploited to improve network…

Ubiquitous computing helps make data and services available to users anytime and anywhere. This makes the cooperation of devices a crucial need. In return, such cooperation causes an overload of the devices and/or networks, resulting in…

Cyber-physical systems, such as mobile robots, must respond adaptively to dynamic operating conditions. Effective operation of these systems requires that sensing and actuation tasks are performed in a timely manner. Additionally, execution…

Accelerator-based heterogeneous architectures, such as CPU-GPU, CPU-TPU, and CPU-FPGA systems, are widely adopted to support the popular artificial intelligence (AI) algorithms that demand intensive computation. When deployed in real-time…

With ever-increasing application of machine learning models in various domains such as image classification, speech recognition and synthesis, and health care, designing efficient hardware for these models has gained a lot of popularity.…

This paper proposes a distributed on-orbit spacecraft assembly algorithm, where future spacecraft can assemble modules with different functions on orbit to form a spacecraft structure with specific functions. This form of spacecraft…

With the ever-growing need of data in HPC applications, the congestion at the I/O level becomes critical in super-computers. Architectural enhancement such as burst-buffers and pre-fetching are added to machines, but are not sufficient to…

Recent years have witnessed a rapid growth of distributed machine learning (ML) frameworks, which exploit the massive parallelism of computing clusters to expedite ML training. However, the proliferation of distributed ML frameworks also…

Nowadays, FPGAs are integrated in high-performance computing systems, servers, or even used as accelerators in System-on-Chip (SoC) platforms. Since the execution is performed in hardware, FPGA gives much higher performance and lower energy…

This work details a hardware-assisted approach for information flow tracking implemented on reconfigurable chips. Current solutions are either time-consuming or hardly portable (modifications of both sofware/hardware layers). This work…