CE research lies not only at the interface of computer science and electrical engineering, but increasingly ties computing together with biology, medicine, chemistry, physics, mechanical engineering, and even environmental engineering.
Our research is ideally positioned to help solve societal problems through the construction of practical systems composed of emerging technologies. We live in a time of both opportunity and crisis. Rising carbon emissions and energy costs are a global problem. Aging populations increasingly strain healthcare resources. Computing technologies are at the heart of many potential solutions to these problems. Emerging technologies in nanoscale and bio-compatible materials hold the promise to increase energy-efficiency and revolutionize healthcare. We also see opportunities in massive information gathering and large-scale computing resources to exploit that information.
We must also address increasing challenges to continued scaling of conventional silicon and to maintaining the dramatic performance growth of past computing systems.
SEALab aims at leveraging emerging technologies (with emphasize on 3D integration and emerging nonvolatile memory) for edge-cutting applications (e.g., machine learning and bioinformatics).
SEALab distinguishes itself by strong cross-layer researches going from device to application, and works on circuit design, EDA tools, and computer architecture at the same time. Recent research highlights are as follows:
3D Design & Design Automation
The architecture we proposed in 2012 turns to the world-first 3D GPU on the market (AMD Fury X). Our recent researches including cost analysis for 2.5D/3D integration, heterogonous integration, and 3D based hardware security study.
Modeling, Architecture and Application for Emerging Nonvolatile Memory
We help the community to understand NVM’s pros and cons for better utilizing them to improve the future computing systems, e.g., IoT, GPGPU, NoC, and Data Center. We are also interested in utilizing NVM’s nonvolatility feature for normally-off computing, check-pointing, and persistent memory.
Energy-efficient Hardware for Machine Learning and Neuromorphic Computing
We explore optimizing machine learning applications on parallel and heterogonous architecture as well as reconfigurable fabric. We also leverage emerging technologies for machine learning and bio-inspired applications, e.g., 3D stacked high bandwidth memory, low-power NVM.
The TFET employs atomically-thin semiconducting channel material & quantum mechanical tunneling, operates at a supply voltage of only 0.1 V with high ON/OFF current ratio, and lowers power dissipation by over 90% compared to other transistors.
Wolski, along with researchers from Cornell and U. of Buffalo, received $5M for a project that aims to build a federated cloud, deployed at the 3 collaborating universities, known as the Aristotle Cloud Federation.
Research lab: Microprocessor Test and Validation
Tell Us About Your Research: We investigate the use of open source software technologies to build scalable systems. Cloud computing and large-scale high-performance computing depend on software innovations that enable the computational, communication, and storage capabilities of many computers and networks to be used together. Our group studies the system-building principles needed to create such amalgamations empirically, by building experimental software systems that can be tested and studied in a scientific context.
What do Find Particularly Rewarding about your Research?: Working with students to investigate new answers and solutions to pressing and difficult problems is by far the most rewarding part of research for me.