With the rise of the Cloud, Artificial Intelligence, and The ‘Internet of Things’ (the foreseeable future in which not just humans but all our devices are connected to each other via the Internet), the need to store data has been forecasted to increase to ~163 ZB (trillion GB) by 2025. And in order to store such a vast amount of data, non-volatile memory (NVM) devices will be needed.
Consequently, by the year 2025, NVM will represent an $82.3 billion market.
Currently available conventional memory technologies face serious challenges in addressing these needs due to increased energy consumption, scalability limitations, and limited bandwidth.
Therefore, there is an urgent demand to develop new memory devices, circuits, and architectures to support these needs. Dr. Rashmi Jha, University of Cincinnati (UC) College of Engineering and Applied Science (CEAS) Associate Professor of Electrical Engineering and Computer Science, aims to develop new types of memory devices that will be massively scalable, energy-efficient, and fast which will open tremendous opportunities to integrate them in existing memory architectures to enable various futuristic applications.
Funded by the National Science Foundation (NSF), Dr. Jha’s UC group will collaborate with Dr. Swaroop Ghosh’s team at Penn State University to achieve the objectives of this research. Combined NSF-funding for UC and Penn State totals $450K and runs from August 1, 2017 through July 31, 2020 (estimated).
Dr. Jha explains, “Our latest and greatest technology advancements (such as AI, the Internet of Things, Machine Learning, and Deep Neural Networks) require massive data storage and in-memory computing, making new computer architectures like Tensor Processing Units (TPU) of the utmost necessity. Availability of on-chip memory and the ability to do in-memory computing will be critical to address the massive computer resources demands for training and inferencing using these architectures.”
When integrated with smartphones, Dr. Jha’s technology will allow the devices to not only store more data (i.e. pictures and videos) through low power consumption, but they will also be able to perform more complex computing tasks. Possible tasks include; facial cues recognition, behavior recognition, and smart health monitoring with actionable insights. The integration of these systems in wearable computing platforms (wearable sensors, smart watch, smart-patch, smart-belt, smart-Knee caps, Point-of-Care devices) will help programmers to implement more complex algorithms that can be used for applications such as monitoring rehabilitation after injuries, or skin cancer detections.