This project focuses on developing a plant growth monitoring system for space exploration missions using the ARM Cortex-M0 microcontroller core. The projects aim to develop a SOC based on ARM M0 core for interactive plant monitoring by interfacing AHB lite, GPIO, timers, and communication protocols such as UART, I2C, SPI, and co-processors.  This project also proposes two co-processors for interactive plant monitoring and control. One AI co-processor for classification and prediction of plant and environmental data.

Conventional healthcare is expensive and reliant on the physical presence of the patients. Continuous health monitoring tracks vital health parameters like heart rate, blood pressure, etc. While these work well in measuring the parameters, modern-day devices rely on the cloud to compute and interpret data. This results in an increase in data transfer between the device and the cloud, and if this connection breaks, there can be no interpretation of data. Hence, there is a need to shift the computation to the hardware, referred to as "Edge Computing".

Advancements in electronics, wireless communications, and sensing technologies have made possible a multitude of smart sensing features in automotives. Integrating high-frequency sensors, digital signal processors and hardware accelerator engines on a single system on a chip (SoC) enhances sensing computation potential of radar sensors utilized in automotives.

We propose the A-Cube design methodology to create medical decision support on the edge. The design and implementation of an atrial fibrillation detector hardware core was selected as a proof-of-concept study. To facilitate the required atrial fibrillation functionality, we adopted an established AI model, based on Long Short-Term Memory (LSTM) technology for hardware implementation. The adaptation was done by varying design parameters such as data window and the number of LSTM units.

Battery storage systems are an important source for powering emerging clean energy applications. The Battery Management System (BMS) is a critical component of modern battery storage, essential for efficient system monitoring, reducing run-time failures, prolonging charge-discharge lifecycle, and preventing battery stress or catastrophic situations. The BMS performs functionalities such as data acquisition and monitoring, battery state estimation, cell equalization, and charge protection, making it computationally intensive to manage large scale battery storage.

Autonomous mobile robots (AMRs) have been proven useful for smart factories and have the potential to revolutionize critical missions, such as disaster rescue. AMRs can perceive the environment, plan for assigned tasks, and act on the plan. Motion control is critical to the robot's action, which is accomplished through trajectory optimization to refine the robot's states using a physics model. However, the high computational complexity of trajectory optimization poses significant challenges for AMRs with limited power and computing resources.

This project focuses on developing a plant growth monitoring system for space exploration missions using the ARM Cortex-M0 microcontroller core. The projects aim to develop a SOC based on ARM M0 core for interactive plant monitoring by interfacing AHB lite, GPIO, timers, and communication protocols such as UART, I2C, SPI, and co-processors.  This project also proposes two co-processors for interactive plant monitoring and control. One AI co-processor for classification and prediction of plant and environmental data.

CMOS image sensors (CIS) play a crucial role in the imaging industry. CIS produces low-quality images in low-light conditions. Single Photon Avalanche Diode (SPAD) is a device used for low-light imaging because of its ability to detect single photons of light. To detect a single light photon, SPAD is biased above its breakdown voltage (Gieger mode). When the photon hits the active area during Geiger mode, a significant reverse current (avalanche current) is observed.

Efficiency in hardware is vital as neural network models become more complex to tackle challenging problems, and optimizing ML hardware architectures has become a crucial research area. Scientists around the world, such as particle physicists at CERN need to accelerate their ML models in FPGA or custom ASICs for various applications including compressing the gigantic amount of data generated by the detectors at Large Hadron Collider (LHC).

Neural networks have enabled state-of-the-art approaches to achieve impressive results on many image processing and analysis tasks. However, while gigapixel images are gaining ground in domains like satellite imaging and digital pathology, feeding neural networks directly with these ultra-high-resolution images is still computationally challenging. With a growing number of high-resolution computer vision applications being proposed, the need for an efficient and powerful AI acceleration system targeting gigapixel images rises.