Our Integrated Systems department innovates, designs, and prototypes high-performance intelligent distributed systems, applications, and services on complex, large-scale communication networks like 5G and beyond. We develop next-generation wireless technologies for sensing the world, localizing critical assets, and improving the capacity, coverage, and scalability of communication networks like 5G and beyond.
New application needs have always sparked human innovation. A decade ago, cloud computing enabled high-value enterprise services with a global reach and scale but with several minutes or seconds of delay. Large-scale services like enterprise resource planning (ERP) were a corner-case scenario, often designed as one-off systems. Today, applications like social networks, automated trading, and video streaming have made large-scale services the norm rather than the exception. In the future, advances in 5G networks and an explosion in smart devices, microservices, databases, networking, and computing tiers will make services so complex that humans cannot tune or manage them.
The sheer scale, dynamic nature, and concurrency in services on 5G slices will require them to be intelligent and autonomic. They will need to continuously self-assess, learn, and automatically adjust for resource needs, data quality, and service reliability. The need for increased efficiency and reduced latency between measurement and action drives our design of real-time distributed systems for feature extraction, computation, and machine learning on multimodal streaming data. We are conducting extensive research on creating end-to-end solutions using multimodal sensing technologies in the retail, public safety, and transportation domains.
Our 5G cellular network research encompasses the development of technologies on the Radio Access Network (RAN), the mobile edge, and the 5G LAN. Within the RAN, we are developing technologies that optimize massive MIMO/MU-MIMO deployments and millimeter-wave access (e.g., transmission at 28 GHz to nomadic/mobile users). At the mobile edge (MEC), we focus on virtualization, scalability, and cloud deployment of appropriate services. Our 5G LAN research extends the benefits of 5G slicing technology to enterprise LANs to position the enterprise as the new MEC.
Read our news and publications from our world-class team of researchers from our Integrated Systems department.
In the evolving Artificial Intelligence (AI) era, the need for real-time algorithm processing in marine edge environments has become a crucial challenge. Data acquisition, analysis, and processing in complex marine situations require sophisticated and highly efficient platforms. This study optimizes real-time operations on a containerized distributed processing platform designed for Autonomous Surface Vehicles (ASV) to help safeguard the marine environment. The primary objective is to improve the efficiency and speed of data processing by adopting a microservice management system called DataX. DataX leverages containerization to break down operations into modular units, and resource coordination is based on Kubernetes. This combination of technologies enables more efficient resource management and real-time operations optimization, contributing significantly to the success of marine missions. The platform was developed to address the unique challenges of managing data and running advanced algorithms in a marine context, which often involves limited connectivity, high latencies, and energy restrictions. Finally, as a proof of concept to justify this platforms evolution, experiments were carried out using a cluster of single-board computers equipped with GPUs, running an AI-based marine litter detection application and demonstrating the tangible benefits of this solution and its suitability for the needs of maritime missions.
Deep learning based joint source-channel coding (JSCC) has demonstrated significant advancements in data reconstruction compared to separate source-channel coding (SSCC). This superiority arises from the suboptimality of SSCC when dealing with finite block-length data. Moreover, SSCC falls short in reconstructing data in a multi-user and/or multi-resolution fashion, as it only tries to satisfy the worst channel and/or the highest quality data. To overcome these limitations, we propose a novel deep learning multi-resolution JSCC framework inspired by the concept of multi-task learning (MTL). This proposed framework excels at encoding data for different resolutions through hierarchical layers and effectively decodes it by leveraging both current and past layers of encoded data. Moreover, this framework holds great potential for semantic communication, where the objective extends beyond data reconstruction to preserving specific semantic attributes throughout the communication process. These semantic features could be crucial elements such as class labels, essential for classification tasks, or other key attributes that require preservation. Within this framework, each level of encoded data can be carefully designed to retain specific data semantics. As a result, the precision of a semantic classifier can be progressively enhanced across successive layers, emphasizing the preservation of targeted semantics throughout the encoding and decoding stages. We conduct experiments on MNIST and CIFAR10 dataset. The experiment with both datasets illustrates that our proposed method is capable of surpassing the SSCC method in reconstructing data with different resolutions, enabling the extraction of semantic features with heightened confidence in successive layers. This capability is particularly advantageous for prioritizing and preserving more crucial semantic features within the datasets.
In 2008, parallel computing posed significant challenges due to the complexities of parallel programming and the bottlenecks associated with efficient parallel execution. Inspired by the remarkable scalability achieved by networking and storage systems in handling extensive packet traffic and persistent data respectively by leveraging best-effort service, we proposed a new and fundamentally different approach of best-effort computing.Having observed that a broad spectrum of existing and emerging computing workloads were from applications that had an inherent forgiving nature [2], [5], we proposed best effort computing. The new approach resulted in disproportionate gains in power, energy and latency, while improving performance. While contemplating the concept of best-effort computing [2], we noticed the resurgence of convolutional neural networks, which generated approximate but acceptable outcomes for numerous recognition, mining, and synthesis tasks. The lead author of this retrospective had previously conducted research on neural networks for his doctoral dissertation over a decade ago, and the reemergence of neural networks proved both surprising and exciting. Recognizing the connection between best-effort computing and convolutional neural networks, in 2008 we embarked on developing a programmable and dynamically reconfigurable convolutional neural network capable of performing best effort computing for various machine learning tasks that inherently allow for multiple acceptable answers. This combination of our thoughts on best-effort computing and the gradual evolution of convolutional neural networks (deep neural networks emerged much later) culminated in our 2010 ISCA work on dynamically reconfigurable convolutional neural networks.
