The increasing use of artificial intelligence on smartphones, Internet of Things devices, edge servers, and other distributed platforms has created new opportunities for intelligent applications but has also intensified concerns regarding the privacy of machine-learning data. Conventional centralized machine learning r...
Dr. Pankaj Kumar· Zenodo (CERN European Organi...· 0 citations
The increasing use of artificial intelligence on smartphones, Internet of Things devices, edge servers, and other distributed platforms has created new opportunities for intelligent applications but has also intensified concerns regarding the privacy of machine-learning data. Conventional centralized machine learning r...
Dr. Pankaj Kumar· Zenodo (CERN European Organi...· 0 citations
This work provides a comprehensive roadmap for developing intelligent, sustainable and resilient EV charging ecosystems that will resolve technical, economical, and ecologicalconsiderations while protecting the privacy of data and securing the system.
Narendra Kumar· International Journal of Dig...· 0 citations
Mobile Edge Computing (MEC) and Mobile Computation Offloading (MCO) help IoT devices with limited computational capabilities and battery by offloading tasks to the nearest resource-rich servers in MEC. Deciding to execute the tasks at the user device or the edge server can be optimized by AI techniques such as Deep Rei...
Hala Elhadidy, H. Saleh, R. Rizk et al.· Cluster Computing· 0 citations
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This article presents an energy-efficient and process-, voltage-, and temperature (PVT)-robust time-domain (TD) compute-in-memory (CIM) macro for edge artificial intelligence (AI) devices. It features: 1) a PVT inner-tracking (PIT) technique that aligns the PVT responses of TD computation and TD quantization, deliverin...
Yuanzhe Zhao, Yu-Heng Wang, Heng Xie et al.· IEEE Journal of Solid-State...· 0 citations
The rapid proliferation of intelligent sensors has led to increased latency and privacy risks when processing data on a remote server. This work proposes a spin-transfer torque magnetic random access memory (STT-MRAM) compute-in-memory (CIM) macro to enhance inference efficiency for an artificial intelligence (AI) mode...
Jia-le Cui, Ting-xuan Shi, Shuyu Wang et al.· IEEE Journal of Solid-State...· 0 citations
Rate splitting multiple access (RSMA) is a promising multiple access (MA) technology that can be used to enhance the performance of military multiple-input multiple-output (MIMO) systems. By virtue of its rate-splitting method, this technology can flexibly regulate the interference management strategy, achieving superi...
Nonvolatile AI-edge processors based on near-memory-compute (nvNMC) enable energy-efficient multiply-and-accumulate (MAC) operations with short wakeup latency for edge inference operations. Lossless compression is required for floating-point (FP) neural network (NN) models under on-chip memory capacity constraints; how...
De-Qi You, W. Khwa, Bo Zhang et al.· IEEE Journal of Solid-State...· 0 citations
As businesses strive to improve efficiency, reduce latency, and leverage the power of data, understanding the strengths and limitations of these two computing paradigms is crucial. Full article: https://davidohnstad.com/edge-computing-vs-cloud-computing-which-is-the-future/
David Ohnstad· Zenodo (CERN European Organi...· 0 citations
WHAT THIS PACKAGE DOES This package goes with the revised Paper-1 manuscript "Exact Finite-Sample Permutation Moments for Walsh Interaction-Order Energies: A Projector Specialization of Quadratic-Form Randomization Theory" (v1.1). It contains the full open-source code, exhaustive verification, benchmarks, and environme...
Roshankumar chandaliya, Roshankumar chandaliya· Zenodo (CERN European Organi...· 0 citations
The future of the field lies in the transition from single-modality visual perception to multi-dimensional collaborative detection systems, and this review serves as a comprehensive reference for optimizing object detection algorithms tailored for complex remote sensing and low-altitude security environments.
Ke Gu· International Conference on...· 0 citations
The rapid expansion of edge computing and the Internet of Things (IoT) has transformed the deployment of intelligent applications by shifting computation from centralized cloud infrastructures to resource-constrained edge devices. Although this paradigm enables low-latency processing, reduced bandwidth consumption, and...
Georgiana Petridou· Zenodo (CERN European Organi...· 0 citations
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduOct 2, 2026
