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.
Krintz has taken her engineering expertise out of the lab and is sharing it with growers to help with food sustainability. The award for the hybrid cloud-based sensor system for simplifying and automating agriculture analytics comes with a $25,000 grant.
The award was presented to Sherwood, "for contributions to novel program analysis advancing architectural modeling and security." It acknowledges outstanding contributions to computer architecture made within the first 20 years of a career.
Tell Us About Your Research: My research focuses on automated verification techniques and their application to software. As computer systems become more pervasive, their dependability becomes increasingly important. As a result, there is an ongoing shift in focus, both in academia and industry, from performance to dependability. The size and complexity of the software systems nowadays inevitably lead to errors during both design and implementation phases. The goal of our research at VLab is to develop verification techniques that will help developers in identifying errors in software. Recently, we have developed a novel approach for finding data model bugs in software applications written using the Ruby-on-Rails framework, where programming errors could lead to loss of data or unauthorized access to data — see research full description