Welcome to HPCAT


We rely on computing in the design of systems for energy, transportation, finance, education, health, defense, entertainment, and overall wellness. However, today's computing systems are facing major challenges both at the technology and application levels. At the technology level, traditional scaling of device sizes has slowed down and the reduction of cost per transistor is plateauing, making it increasingly difficult to extract more computer performance by employing more transistors on-chip. Power limits and reduced semiconductor reliability are making device scaling more difficult – if not impossible – to leverage for performance in the future and across all platforms, including mobile, embedded systems, laptops, servers, and datacenters. Simultaneously, at the application level, we are entering a new computing era that calls for a migration from an algorithm computing world to a learning-based, data-intensive computing paradigm in which human capabilities are scaled and magnified. To meet the ever-increasing computing needs and to overcome power density limitations, the computing industry has embraced parallelism (parallel computing) as the only method for improving computer performance. Today, computing systems are being designed with tens to hundreds of computing cores integrated into a single chip and hundreds to thousands of computing servers based on these chips are connected in datacenters and supercomputers. However, power consumption remains a significant design problem, and such highly parallel systems still face major challenges in terms of energy efficiency, performance, and reliability.

Professor Louri and his team investigate novel parallel computer architectures and technologies which deliver high reliability, high performance, and energy-efficient solutions to important application domains and societal needs. The research has far-reaching impacts on the computing industry and society at large. Current research topics include: (1) the use of machine learning techniques for designing energy-efficient, reliable multicore architectures, (2) scalable accelerator-rich reconfigurable heterogeneous architectures, (3) emerging interconnect technologies (photonic, wireless, RF, hybrid) for network-on-chips (NoCs) & embedded systems, (4) future parallel computing models and architectures including Convolutional Neural Networks (CNNs), Deep Neural Networks (DNNs), near data computing, approximate computing, and (5) cloud and edge computing.

Current Research

Machine Learning for High Performance Reliable NoCs

With continued aggressive technology scaling, Network-on-Chips (NoCs) architectures are facing three major challenges including minimizing power consumption, scaling performance, providing a reliable and robust communication. (more…)

Energy-Efficient Scalable Multicore Architectures

Over the last decade, Moore’s Law has slowed, while Dennard Scaling has ended. The end of voltage scaling has made power dissipation the fundamental barrier to scaling computing performance across all platforms. (more…)

Neural Networks Accelerator and Applications

Neural networks (NNs) have been successfully implemented in modern artificial intelligence (AI) applications ranging from image processing to speech recognition to natural language processing. (more…)

Approximate Communications

Recent research has shown that on-chip data movement consumes much more power than computation and this trend will continue in the future. Additionally, some algorithms and applications, such as machine learning… (more…)

Accelerator-Rich Heterogeneous Architectures

In the dark silicon era, only a fraction of transistors on a chip can be switched simultaneously due to constrained power budget. To improve energy-efficiency, general-purpose cores are augmented with specialized hardware or accelerators. (more…)

Emerging Interconnect Technologies for NOCS

There is a shift from multi-core to many-core architectures containing hundreds to a thousand cores on a single chip. However, traditional on-chip metallic interconnects require excessive power as technology keeps scaling. (more…)

Past Research

HPCAT Members

Open Positions

We are always seeking talented researchers that are ambitious and want to know more

Learn More

Related Organizations