For decades, Moore’s Law and its partner Dennard Scaling have driven technology trends that have enabled exponential performance improvements in computer systems at manageable power dissipation. With the slowing of Moore/Dennard improvements, designers have turned to a range of approaches for extending scaling of computer systems performance and power efficiency. Unfortunately, these scaling gains come at the expense of degraded hardware-software abstraction layers, increased complexity at the hardware-software interface, and increased challenges for software reliability, interoperability, and performance portability. My work explores the way forward for computer systems designers in this “Post-ISA” era of shifting abstractions. My group looks hardware and software design issues for specialization/heterogeneity and methods for formal verification. We are also increasingly focused on the hardware/software systems issues of Quantum Computing.

Darya Mikhailenko is pursuing a PhD in Electrical and Computer Engineering at the University of Rochester under the supervision of Prof. Engin Ipek. Her interests cover areas of energy-efficient computer architectures, machine learning for computer vision applications, memory management, accelerators, and architecture modeling for emerging technologies. Darya worked as a research assistant at Nanoelectronics Research Laboratory (Purdue University, U.S.A.) and Bioinspired Microelectronics Systems Laboratory (Nazarbayev University, Kazakhstan) on the implementation of brain-computer architecture to actualize deep neural networks. Her recent work was dedicated to techniques to minimize data movement energy by exploiting asymmetric on- and off-chip interconnects. Currently, she researches microarchitecture level optimizations for AR/VR applications.

Darya is looking for internships in the field of AR/VR related but not limited to computer architecture and machine learning algorithms.

Charu Kalra is a PhD student in the Computer Engineering Department at Northeastern University. She is part of the Northeastern University Computer Architecture Research (NUCAR) group under the direction of Prof. David Kaeli. Her research interests include GPU compilers, software reliability, machine learning, workload characterization, and performance evaluation of GPU systems. Her PhD thesis focuses on design and evaluation of compiler-based techniques to predict and improve reliability of GPU applications. In 2014, Charu was featured on NVIDIA's 'Women Who CUDA' list. She has also pursued internships at AMD and AMD Research in the past.

Atefeh Mehrabi is a PhD student at ECE department of Duke University. She is co-advised by Prof. Sorin and Prof. Lee. Her research focuses in the area of computer architecture. More specifically, she is exploring and trying to tackle challenges of emerging reconfigurable accelerators. She got her B.Sc degree from University of Tehran in 2016 in Electrical Engineering and her M.Sc degree in Computer Engineering from Duke in 2018.