This paper proposes the Mobile AI Stack, a mobility-aware architectural framework for large-scale mobile intelligence systems, integrating energy networks, energy-efficient chips, infrastructure, dist…

The paper proposes a framework to harvest unused computation resources on AI chips for general-purpose tasks using neural architecture search and approximation techniques.

This paper presents a tutorial-and-survey on integrating agentic AI into Next-Generation Networks (NGNs), addressing the gap in protocol integration, evaluation, and standardization alignment.

HighTide is an evolving, AI-assisted, open-source benchmark suite for VLSI design, providing a comprehensive and scalable platform for hardware development.

This paper systematically analyzes the complex design space of hybrid multi-agent systems combining on-device and cloud AI models, finding that the optimal architecture is highly task-dependent and th…

This paper tests different INT8 quantized Multi-Layer Perceptron models for malware detection on Android devices, achieving high accuracy with reduced energy consumption.

This paper presents two loop cache architectures for RISC-V processors to reduce instruction fetches and energy consumption during AI inference.

The paper introduces Chimera, a highly efficient and scalable MCU designed for ultra-low-power edge AI inference, achieving 3.1 TOPS/W by integrating a dedicated transformer accelerator and a QoS-guar…

The paper introduces a novel hardware aging attack that exploits the commutative properties of addition to induce unbalanced stress on AI accelerator transistors, significantly degrading model accurac…

This paper proposes Autogenic network management, a self-programming extension to agentic AI for next-generation network management in 6G networks.

This paper provides the first comprehensive review of threats and defenses specifically targeting on-device AI inference, revealing a significant imbalance where certain attack types, like adversarial…

This paper proposes a new communication framework, TokCom, for 6G wireless networks where tokens from large language models become the fundamental entities for information exchange.

This paper proposes a mechanical auditing approach for verifying AI functions in wireless networks using machine-verifiable specifications.

This paper characterizes the performance of vision-language model inference on the Qualcomm SM8750 using FastVLM-0.5B as a case study, showing significant speedups and energy savings for different pha…

The paper introduces VEHBench, an engineering-native diagnostic benchmark for LLM-assisted VEH design, featuring 763 literature-grounded tasks.

This paper argues for the importance of modularity and heterogeneity in AI architectures, contrasting the Transformer model with the structure of the cortex.