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

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.

Biological computing elements are mimicked by developing novel nanoelectronic artificial neurons and synapses and assembled in brain-inspired cortical circuitries and architectures to create artificial brains and neuromorphic processors

Illustration of a lock in the middle of a circuit board

By identifying unique characteristics of nanoelectronic emerging logic and memory devices we can design Integrated Circuits are inherently more secure against hardware attacks.

Trusted and Secured Microelectronics Hardware

Due to complex supply-chain of microelectronics integrated circuits (IC) design and fabrication, hardware security has emerged out to be one of the prime concerns. This project seeks to develop novel ways to design and fabricate secure microelectronics circuits using emerging nanoelectronics logic and memory devices. The students will be involved in the design of ICs, simulations, fabrication, and testing. A fundamental question we will try to answer is how can design itself be made more trust-worthy and resilient? Can emerging nanoelectronic memory and logic devices offer any inherent benefit in this area?

Rashmi Jha

Professor, CEAS - Electrical and Computer Engineeri

385 MANTEI

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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