Automating application-driven customization of ASIPs: A survey
The rapid advancements and stringent requirements of modern embedded computing systems have led to a surge in the demand for customized processors that can efficiently cater to specific application needs. This survey paper delves into the realm ...
Machine learning-based computation offloading in multi-access edge computing: A survey
The advancement of technology towards the realization of the evolving mobile computing paradigm brings a rapid paradigm shift in its usage, especially in the Internet, computation, and communications, that has a profound impact on businesses, ...
Code generation for Security and Stability Control System based on extended reactive component
The Security and Stability Control System (SSCS) is developed to ensure the safe and stable operation of power grids, effectively mitigating failure propagation through emergency control strategies. However, due to the diversity, complexity, and ...
CLFLDP: Communication-efficient layer clipping federated learning with local differential privacy
Privacy preserving is a severe challenge in machine learning and artificial intelligence. Recently, many works have been devoted to solving this problem by proposing various federated learning frameworks and introducing local differential ...
VSPAKE: Provably secure verifier-based PAKE protocol for client/server model in TLS ciphersuite
Nowadays, password-authenticated key exchange (PAKE) protocols have actually been widely used in our daily life to provide security assurance, by which two parties can achieve mutual authentication and cryptographic session key establishment via ...
Attribute-based searchable encryption with decentralized key management for healthcare data sharing
In this paper, we address the secure sharing of sensitive healthcare data in blockchain-based healthcare. As a form of sensitive information, healthcare data is often encrypted before being uploaded to cloud servers. Extensive research has been ...
Homogeneous teacher based buffer knowledge distillation for tiny neural networks
Knowledge Distillation (KD) has shown great promise in improving the performance of tiny neural networks. Most existing KD methods have the large teacher–student discrepancy, thus, students hardly learn useful knowledge and may not achieve ...
Ensuring consistent recovery under power failure with minimal NVM write overhead
Intermittent embedded devices and systems are widely used in various scenarios, but they often experience power failures due to unstable power supplies. Non-volatile memory (NVM) is gaining popularity in embedded systems due to its byte-...
VPSS: A DAG scheduling heuristic with improved response time bound
Real-time and embedded systems are shifting from single-core to multi-core platforms, on which software must be parallelized to fully utilize the computation power of multi-core hardware. Most current real-time parallel tasks can be modeled as ...
An efficient multi-task learning CNN for driver attention monitoring
Driver Monitoring System (DMS), usually equipped with a camera, is an emerging vehicle safety system that can monitor driver attentiveness and trigger timely alarms when signs of inattention are detected. Since a single indicator (e.g., eye blink ...
HiEval: A scheduling performance estimation approach for spatial accelerators via hierarchical abstraction
Workload scheduling strategy, referred to as mapping, plays a vital role in exploring hardware spatial accelerator performance. Evaluating all possible mappings experimentally is infeasible, thus we propose HiEval, to efficiently and accurately ...
Parametric WCET as a function of procedure arguments: Analysis and applications
Traditional Worst-Case Execution Time analysis derives an upper-bound to the execution time of a program for any possible combination of its software and hardware parameters. In comparison, Parametric Worst-Case Execution Time analysis derives a ...
Doppel: A BFT consensus algorithm for cyber-physical systems with low latency
The integration of blockchain technology with Cyber-Physical Systems (CPS) has gained significant attention across various industry domains such as manufacturing, healthcare, transportation, and energy management. The consensus mechanism serves ...
HCEC: An efficient geo-distributed deep learning training strategy based on wait-free back-propagation
Valuable data is often distributed across multiple data centers (DCs). Deep learning (DL) tasks, constrained by privacy regulations, utilize local training and model averaging to facilitate collaborative training across multiple DCs. However, the ...
Hybrid Privacy Preserving Federated Learning Against Irregular Users in Next-Generation Internet of Things
While federated learning (FL) is a well-known privacy-preserving (PP) solution, recent studies demonstrate that it still has privacy problems and vulnerabilities, particularly in the context of the Next Generation Internet-of-Things (NG-IoT). ...
Time-predictable task-to-thread mapping in multi-core processors
The performance of time-predictable systems can be improved in multi-core processors using parallel programming models (e.g., OpenMP). However, schedulability analysis of parallel applications is a big challenge due to their sophisticated ...
Real-time rate control of WebRTC video streams in 5G networks: Improving quality of experience with Deep Reinforcement Learning
Adapting to a dynamic environment is a critical challenge in deploying robust systems that will be tasked with transmitting media streams in 5G networks. The Web Real-Time Communication (WebRTC) protocol is one of the most popular solutions for ...
