Current Research Projects
As we near the end of Dennard scaling, traditional performance and power scaling benefits primarily derived from semiconductor process technology improvements no longer exist. At the same time, transistor density improvements continue; the result is the “dark silicon” problem in which chips now have more transistors than a system can fully power at any point in time. Constrained by power consumption, hardware acceleration in the form of datapath and control circuitry customized to particular algorithms or applications offers a promising approach to deliver orders of magnitude performance and energy benefits compared to general purpose computing.
To learn more about our ongoing projects in hardware accelerators, click here.
Our group adopts a full stack approach to deep learning. We explore deep learning optimization algorithms, deep neural network design, benchmarking of deep learning systems, hardware acceleration of DNNs, and more. Our targeted problem domains span image classification to speech recognition and machine translation.
To learn more about our ongoing projects in deep learning, click here.