Microelectronics and Integrated-Systems with Neuro-centric Devices (MIND) Lab

MIND Lab provides an excellent research environment to the students. The students will work in the team of graduate, and undergraduate students, and PI.

Neuromorphic Computing and Artificial Intelligence

Artificial Intelligence (AI) has caught significant research attention. However, AI algorithms are compute resource intensive. Therefore, much emphasis has been devoted on developing specialized neuromorphic System on Chip (SoC) for accelerating AI recently. Some examples of these neuromorphic-SoC include Google’s TPU, Intel’s Nirvana and Loihi, and IBM’s TrueNorth. In spite of these significant advances, the performance of these processors are limited by the lack of novel reconfigurable memory devices that can mimic the adaptive characteristics of biological synapses. MIND lab researchers have invented new type of memory devices, referred to as, Low-Energy Evolutionary Channel Electronic Synapses (LEECHES) that can provide lucrative options for neuromorphic SoC. The student working on this project will work on this cutting-edge technology and develop neuromorphic SoC utilizing these novel memory devices. The student will perform the following tasks:

Testing and Modeling of LEECHES in Verilog/VHDL
Modeling and simulation of neural circuit modules around LEECHES
Implementation of learning algorithms and performance evaluation of this novel neuromorphic SoC
Publication and presentations.
Training data collection on drones fitted with cameras and multiple sensors.

Trust and Security Issues in CMOS Active Pixel Sensors

CMOS Active Pixel Sensors (APS) serves the backbone of all digital cameras and video systems. Most of the autonomous systems heavily rely on data from cameras for making decisions. However, APS are prone to being tempered and attacked during operation. The student working on this project will evaluate various possible attacks on CMOS APS and remedies. The student will work on:

Simulation of CMOS APS in HSPICE using 45nm transistor nodes.
Identifying various techniques of introducing Trojans in APS.
Understanding the impact of these Trojans on images generated from APS.
Impact of this tempering on the behavior of autonomous systems.
Identifying ways to mitigate it.
Publication and presentations.

Rashmi Jha

Professor, CEAS - Elec Eng & Computer Science

385 Engineering Research Cntr

513-556-1361

jhari@ucmail.uc.edu

Dr. Rashmi Jha is Professor in Electrical Engineering and Computer Science Department at the University of Cincinnati. She was an Associate Professor at the University of Cincinnati from 2015-2020. She worked as an Assistant Professor and then Associate Professor in Electrical Engineering and Computer Science Department at the University of Toledo from 2008 to 2015. Before this, she worked as a Process Integration Engineer for 45 nm/32 nm High-k/Metal Gates based Advanced CMOS technology at Semiconductor Research and Development Center, IBM, East Fishkill New York between 2006-2008. She finished her Ph.D. and M.S. in Electrical Engineering from North Carolina State University in 2006 and 2003, respectively, and B.Tech. in Electrical Engineering from Indian Institute of Technology (IIT) Kharagpur, India in 2000. She has more than 18 years of experience in the areas of Solid State Electronics and Nanoelectronic Device Design, Modeling, Fabrication, Process Integration, Electrical Characterization, Data Analysis, Circuit Design and Simulations. She has been granted 13 US patents and has authored/co-authored several publications in the areas of computing devices, circuits, and algorithms. She has been a recipient of AFOSR Summer Faculty Fellowship Award in 2017, CAREER Award from the National Science Foundation in 2013, IBM Faculty Award in 2012, IBM Invention Achievement Award in 2007, Materials Research Society's Graduate Student Award in 2006, Applied Materials Fellowship Award in 2005-2006, and the best student paper award nomination in IEEE International Electron Devices Meeting (IEDM) in 2005. Her current research interests span all the way from devices to systems and algorithms. Particularly, she is interested in Artificial Intelligence (AI), Low-Power Neuromorphic Systems, CMOS and other emerging logic and memory devices ( such as Resistive Random Access Memory Devices, Spintronics, and other memristive devices), On-die sensors, Cross-Technology Heterogenous Integration and Modeling, Cybersecurity with emphasis on Hardware Security, Additive, Flexible, and Wearable Electronics, Nanoelectronics, Neuroscience and Neuroelectronics, Bio-Inspired Computing and Systems.

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