edge computing

Future Trends of AI, Big Data and Digital Business Transformation

artficial intelligence (AI) and big data analytics have moved from specialised technical functions to the core of how organizations compete, create value and organise work. This concluding chapter looks ahead. It examines the technological, organizational, regulatory and societal trends that are likely to shape the nex...

STEER: Reducing Inference Cost in Relational Foundation Models through Semantically Informed Sampling

STEER is proposed, a sampling approach that shrinks the inference context by concentrating it on the tables most relevant to the prediction task at hand, and it is shown that it reduces inference context size by about 40% on average while maintaining, and in some cases improving, prediction accuracy.

MOMAT: Mixture of Multiple Atlases for Low-Power Jailbreak Defense of Quantized LLMs

Quantized large language models are increasingly deployed on edge devices for their low latency and energy efficiency. However, model quantization weakens alignment safeguards, leaving qLLMs (quantized large language models) highly vulnerable to jailbreak attacks. To address this challenge, we present MOMAT (Mixture of...

Autoscaling of Internet of Things applications in serverless edge computing

SpikeEdge: A Residual Spiking Neural Network Architecture With Edge-Based Data Encoding for Efficient Hardware Design

CAMformer: Binary Associative Memory Is All You Need

Transformer attention mechanisms pose significant scalability challenges due to quadratic complexity in sequence length, and existing accelerators remain bottlenecked by dense arithmetic and data movement. This paper proposes CAMformer, a hardware accelerator that reinterprets attention as an associative memory operati...

A High-Linearity Hybrid-Domain SRAM-CIM Macro With Wide-Margin Voltage-to-Time Interface and Process-Adaptive TDC

SRAM-based computing-in-memory (SRAM-CIM) alleviates the memory-wall bottleneck of the von Neumann architecture, enabling energy-efficient AI edge computing. Current-domain CIM schemes suffer from degraded linearity at low supply voltages, whereas time-domain CIM schemes are highly sensitive to process, voltage, and te...

GA-EMDNet: Graph Attention-Guided Eye Movement Detection Network From EMG Sensor Signals

Investigating and categorizing muscle-generated bioelectrical activity specifically electromyography (EMG) recordings associated with extraocular muscles (EOM) is fundamental for building advanced assistive systems. The dynamic and time-varying nature of these physiological waveforms demands analytical techniques capab...

High-Performance Bayesian Neural Network Inference Accelerator Based on FPGA

Bayesian Neural Networks (BNNs) offer robust uncertainty estimation capabilities through probabilistic modeling, yet their prohibitively high computational complexity and resource consumption limit deployment in edge computing. In this paper, we propose an FPGA-based BNN inference accelerator that optimizes critical mo...

Collaborative Large Model Caching and Inference Offloading With Parameter Sharing in MEC

Pretrained Foundation Models (PFMs) enable highaccuracy inference services but are typically deployed in remote datacenters, resulting in prohibitively high inference delay. Mobile Edge Computing (MEC) can mitigate such high delays by caching PFMs or their fine-tuned variants on cloudlets located close to end users. Ho...

OPTIMIZATION MODEL FOR COMPUTING LOAD BALANCING IN HYBRID IoT NETWORKS

Context. The problem of optimizing computational load balancing in hybrid IoT networks combining cloud, edge, and embedded nodes under limited resources and dynamic topology conditions is addressed. The object of the study is the processes and mathematical models of load balancing in heterogeneous IoT environments.Obje...

YOLO-Based Vehicle Detection and Classification for Multi-Lane Free Flow Systems

Multi-Lane Free Flow (MLFF) systems have emerged as a promising approach to reducing congestion at toll gates by enabling uninterrupted toll transactions without requiring vehicles to stop. To support this implementation, this study proposes a YOLOv9-based vehicle detection and classification system optimized using gri...