Computer Engineering Laboratory
The Computer Engineering Lab at the University of Michigan is the top US institution for total publications in top-tier architecture, circuit, and computer-aided design conferences (ISCA, MICRO, ISSCC, VLSI, DAC, DATE)
Welcome to the CE Lab
The Computer Engineering Lab at the University of Michigan is comprised of a multidisciplinary group of faculty and graduate students who conduct research related to hardware design, computer architecture, computer-aided design, and embedded systems.
Meet the people who make up the CE Lab >
Prospective graduate students
Lab members explore theoretical, experimental and applied aspects of computer design, spanning a broad range of hardware topics, including embedded systems design, hardware security issues, computer architecture and data centers architecture, computer-aided design, testing and validation. The research breadth also encompasses software layers close to hardware: operating systems and compilers.
Visit our prospective student page on the CSE website >
Energy efficiency in self-driving cars
PhD student Vidushi Goyal is working with Prof. Reetuparna Das to make electronics more energy efficient in personal devices and in autonomous vehicles.
Sonic Cyber Attacks on MEMS Accelerometers
PhD student Timothy Trippel is working with Prof. Kevin Fu to demonstrate how specially crafted sounds can be used to launch acoustic injection attacks against the sensors in many IoT devices.
CSE alum Akshitha Sriraman’s dissertation recognized by SIGARCH/TCCA
Sriraman was recognized for contributions enhancing the efficiency and scalability of hardware and software architecture for hyperscale datacenter systems.
U-M spin-off Agita Labs releases always encrypted computing product
TrustForge, based on U-M research spearheaded by Austin and Bertacco, provides users with the ability to protect data using a process called sequestered encryption
ADA researchers present at Design Automation Conference
The two papers present work on improving efficiency for developers working with hardware accelerators and improving training performance of deep recommendation systems.