Due to the probabilistic and uncertain nature of learning-enabled autonomous systems, new assurance technologies appropriate for such systems are becoming critical for their acceptance. Runtime monitors are among the key assurance technologies to ...

Next-generation systems, such as edge devices, will have to provide efficient processing of machine learning (ML) algorithms, along with several metrics, including energy, performance, area, and latency. However, the quickly evolving field of ML makes ...

Future autonomous systems will employ complex sensing, computation, and communication components for their perception, planning, control, and coordination, and could operate in highly dynamic and uncertain environment with safety and security assurance. ...

Hybrid machine learning algorithms that combine deep learning with probabilistic inference techniques provide highly accurate scene perception for robot manipulation. In particular, a 2-stage approach that combines object detection using convolutional ...

The arrival of 5G together with advances in artificial intelligence, machine learning, cloud computing, virtualization, and service orchestration have created a ubiquitous computing model at the network edge, enabling a host of new, AI driven edge ...

ESP is an open-source research platform for heterogeneous SoC design. The platform combines a modular tile-based architecture with a variety of application-oriented flows for the design and optimization of accelerators. The ESP architecture is highly ...

The intrinsic complexity of modern computing systems requires structured methods for analyzing and optimizing application performance. In this context, the Roofline model proposes an intuitive and visual method providing performance insight and ...

With the advent of the Internet of Things, embedded systems have become widely used in various fields. Concurrently, the security of these systems has become a concern for many. However, security features that are already available for high-end systems ...

Many safety-critical real-time systems operate under harsh environment and are subject to soft errors caused by transient or intermittent faults. It is critical and yet often very challenging to apply fault tolerance techniques in these systems, due to ...

Increasingly complex and integrated systems design has led to more timing uncertainty, which may result in pessimism in time-sensitive system design and analysis. To mitigate such pessimism, mixed-criticality (MC) design for real-time systems has been ...

Time-sensitive networking (TSN) is a set of standardised communication protocols developed under the IEEE 802.1 working group. TSN aims to support deterministic communication based on network schedules that are distributively configured. It is widely ...

Machine learning models differ in terms of accuracy, computational/memory complexity, training time, and adaptability among other characteristics. For example, neural networks (NNs) are well-known for their high accuracy due to the quality of their ...

The 3D Vertical Resistive Memory (3D-VRAM) is emerging as a promising non-volatile memory (NVM) technology to construct large capacity main memories in future computing systems. In addition to low energy-efficiency and non-volatility, ReRAM (Resistive ...

Neural networks have been increasingly applied to control in learning-enabled cyber-physical systems (LE-CPSs) and demonstrated great promises in improving system performance and efficiency, as well as reducing the need for complex physical models. ...

The deployment of Internet of Things (IoT) devices in cyber-physical applications has introduced a new set of vulnerabilities. The new security and reliability challenges require a holistic solution due to the cross-domain, cross-layer, and ...

Large, open, public events, such as marathons and festivals, have always presented a unique safety challenge. These sprawling events, which can take up entire city blocks or stretch for many miles, can draw tens to hundreds of thousands of spectators ...

With the rapid progress of artificial intelligence (AI) algorithms, multi-modal deep neural networks (DNNs) have been applied to some challenging tasks, e.g., image and video description to process multi-modal information from vision and language. ...

Priority-aware networks-on-chip (NoCs) are used in industry to achieve predictable latency under different workload conditions. These NoCs incorporate deflection routing to minimize queuing resources within routers and achieve low latency during low ...

Deep Reinforcement Learning (DRL) is substantially resource-consuming, and it requires large-scale distributed computing-nodes to learn complicated tasks, like videogame and Go play. This work attempts to down-scale a distributed DRL system into a ...

Recent years have witnessed the fast development of quantum computing. Researchers around the world are eager to run larger and larger quantum algorithms that promise speedups impossible to any classical algorithm. However, the available quantum ...