Related papers: Exploring the Potential of Wireless-enabled Multi-…
Emerging AI applications such as ChatGPT, graph convolutional networks, and other deep neural networks require massive computational resources for training and inference. Contemporary computing platforms such as CPUs, GPUs, and TPUs are…
The emergence of sixth-generation and beyond communication systems is expected to fundamentally transform digital experiences through introducing unparalleled levels of intelligence, efficiency, and connectivity. A promising technology…
We study a problem of scheduling real-time traffic with hard delay constraints in an unreliable wireless channel. Packets arrive at a constant rate to the network and have to be delivered within a fixed number of slots in a fading wireless…
The manufacturing industry is at the edge of the 4th industrial revolution, a paradigm of integrated architectures in which the entire production chain (composed of machines, workers and products) is intrinsically connected. Wireless…
Given the fast growth of intelligent devices, it is expected that a large number of high-stake artificial intelligence (AI) applications, e.g., drones, autonomous cars, tactile robots, will be deployed at the edge of wireless networks in…
Many emerging Artificial Intelligence (AI) applications require on-demand provisioning of large-scale computing, which can only be enabled by leveraging distributed computing services interconnected through networking. To address such…
Multi-connectivity is emerging as promising solution to provide reliable communications and seamless connectivity at the millimeter-wave frequency range. Due to the obstacles that cause frequent interruptions at such high frequency range,…
The state-of-the-art driving automation system demands extreme computational resources to meet rigorous accuracy and latency requirements. Though emerging driving automation computing platforms are based on ASIC to provide better…
Deploying mixed-precision neural networks on edge devices is friendly to hardware resources and power consumption. To support fully mixed-precision neural network inference, it is necessary to design flexible hardware accelerators for…
Modern Artificial Intelligence (AI) workloads demand computing systems with large silicon area to sustain throughput and competitive performance. However, prohibitive manufacturing costs and yield limitations at advanced tech nodes and…
Artificial intelligence (AI) and machine learning (ML) are nowadays mature technologies considered essential for driving the evolution of future communications systems. Simultaneously, Wi-Fi technology has constantly evolved over the past…
We study the application of emerging chiplet-based Neural Processing Units to accelerate vehicular AI perception workloads in constrained automotive settings. The motivation stems from how chiplets technology is becoming integral to…
High-performance industrial automation systems rely on tens of simultaneously active sensors and actuators and have stringent communication latency and reliability requirements. Current wireless technologies like WiFi, Bluetooth, and LTE…
With the rapid development of smart terminals and infrastructures, as well as diversified applications (e.g., virtual and augmented reality, remote surgery and holographic projection) with colorful requirements, current networks (e.g., 4G…
Generative artificial intelligence (GAI), known for its powerful capabilities in image and text processing, also holds significant promise for the design and performance enhancement of future wireless networks. In this article, we explore…
Wireless networks are increasingly facing challenges due to their expanding scale and complexity. These challenges underscore the need for advanced AI-driven strategies, particularly in the upcoming 6G networks. In this article, we…
The sixth-generation (6G) of wireless networks introduces a level of operational complexity that exceeds the limits of traditional automation and manual oversight. This paper introduces the "Wireless Copilot", an AI-powered technical…
Routing, switching, and the interconnect fabric are essential components in implementing large-scale neuromorphic computing architectures. While this fabric plays only a supporting role in the process of computing, for large AI workloads,…
After Amdahl's trailblazing work, many other authors proposed analytical speedup models but none have considered the limiting effect of the memory wall. These models exploited aspects such as problem-size variation, memory size,…
A key issue in system design is the lack of communication between hardware, software and domain expert. Recent research work shows progress in automatic HW/SW co-design flows of neural accelerators that seems to make this kind of…