Artificial Intelligence (AI) techniques for big data analytics are becoming very important. However, AI software algorithms are computation resource intensive which imposes limitations on their practical applications as the currently available data processors are not well-suited for these needs. For example, training algorithms for AI can take several hours to days for completing the training process. Additionally, some of the other challenges, such as the requirement for huge training datasets, lack of real-time training and multi-modal data fusion capabilities, and limitations for the system to make decisions reliably with limited input data are well-recognized. Many of these problems can be addressed if brain-inspired neuromorphic data processors can be developed. However, it is a non-trivial task because of two primary reasons. First, the cortical circuits in brain is not fully understood and still is a topic of research in the neuroscience community. Second, artificial neuromimetic components for integration in brain-inspired architectures are not yet developed to match the computational efficiency and diversity of biological-brains. It has been identified from current understanding of cortical circuits in biological-brain that a synapse which is a reconfigurable connection between neurons, play pivotal a role in learning and memory formation. The focus of this project is to develop artificial nanoelectronic synaptic devices that can be integrated in neuromorphic architectures. The project provides significant opportunities for training graduate and undergraduate students in understanding and developing neuromorphic processors for AI. A new course on "Neuromorphic Computers for AI" at the graduate level will be developed. Efforts will be made to increase participation of underrepresented groups in STEM by leveraging the program on "Nurturing Educational Readiness and Development from the Start (NERDS)" and through local Association for Computing Machinery (ACM) chapter.
The nanoelectronic synaptic device will be developed by exploiting time-dependent trap dynamics in oxides in conjunction with the transport of intrinsic or extrinsic dopants in a novel gated-Synaptic Memory Device (gated-SMD) configuration. These dynamics will result in an analog potentiation (increase in conductance) and depression (decrease in conductance) as a function of the temporal sequences of voltage-pulses on gate that can be explored for implementing bio-inspired learning algorithms. The objective of the proposed research will be achieved by executing the following specific aims: (1) fabricating gated-SMDs and studying the device characteristics, including potentiation and depression of resistive states on different time-scales as a function of gate-bias and modeling it; (2) understanding the scalability of these devices by large-scale layout designs and comparing and benchmarking the cell sizes against other candidate memory technologies; and (3) developing novel real-time learning algorithms and implementing bio-inspired learning schemes using gated-SMDs for neuromorphic architectures. The intellectual significance of the proposed research lies in knowledge base and a device platform to provide a solution of nanoelectronic synapses for neuromorphic circuits. If successful, the project will yield the following outcomes: (i) a fundamental understanding of gated-SMDs and device models benchmarked against experimental data, (ii) strategies to control potentiation and depression rates of resistive states in gated-SMD by engineering the device parameters, (iii) real-time learning algorithms tailored for gated-SMDs, and (iv) large-scale integration routes for gated-SMDs and scalability data. The achievement of these outcomes will have transformative impact on developing neuromorphic data processors for AI.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
