Mukherjee Chosen for Cadence Women in Technology Scholarship
Mandovi Mukherjee has been chosen for a 2021 Cadence Women in Technology Scholarship. Mukherjee is a Ph.D. student in the Georgia Tech School of Electrical and Computer Engineering (ECE).
Cadence is committed to building a culture that fosters inclusion and embraces diverse backgrounds, experiences, and ideas. Launched in 2018, Cadence’s scholarship program supports and celebrates young women who are starting their careers in technology.
Mukherjee is a member of the Gigascale Reliable Energy Efficient Nanoelectronics (GREEN) Lab, where she is advised by Saibal Mukhopadhyay; he holds the Joseph M. Pettit Professorship in ECE.
Mukherjee’s current research interests include digital pixel circuits and in-memory computing for real-time, high-performance systems. She has worked on cross-layer controllable energy efficient digital pixel circuits in a smart camera platform. Additionally, Mukherjee works on in-memory/near-memory hardware accelerators that are suitable for applications like RF machine learning, general purpose linear algebraic computations, and real-time environments.
Mukherjee received a B.E degree in Electronics and Telecommunication Engineering from Jadavpur University, located in Kolkata, India, in 2012. She also received the M.Tech. degree in Electronics and Electrical Communication Engineering (Microelectronics and VLSI Design) from the Indian Institute of Technology Kharagpur, located in Kharagpur, India, in 2014. Prior to starting her Ph.D., Mukherjee was a test engineer with Cypress Semiconductor Technology India Pvt. Ltd. in Bangalore, India for two years and a design engineer with Sankalp Semiconductor Private Limited in Kolkata, India for two years.
- Mandovi Mukherjee
- Cadence Women in Technology Scholarship
- graduate students
- Georgia Tech
- School of Electrical and Computer Engineering
- Gigascale Reliable Energy Efficient Nanoelectronics Lab
- Saibal Mukhopadhyay
- digital pixel circuits
- in-memory computing
- smart camera
- in-memory/near-memory hardware accelerators
- RF machine learning
- general purpose linear algebraic computations
- real-time environments