Neuromorphic computing aims to emulate the highly adaptive and efficient processing of the biological brain in silicon. Field-Programmable Gate Arrays offer a way to create general-purpose reconfigurable neuromorphic systems that when coupled with High-Bandwidth Memory are able to offer massive parallelism and throughput. Using these advances, we aim to build a general-purpose neuromorphic computing platform be broadly available to the general research community. This real-time system is reconfigurable, operates in real-time, and implements highly flexible neural dynamics with up to 160 million neurons and 40 Billion synapses. The first part of the thesis discusses the overall system algorithm and investigates the organization of creating an interface between a potential user and the entire reconfigurable platform. We discuss the creation of an intermediate representation for our reconfigurable system. We show the algorithms used in order to map from an intermediate representation of the network into either the High-Bandwith Memory or the python simulator. Finally, we discuss optimization algorithms that can be used to improve both memory savings and execution speed on the network. The second part of the thesis focuses on an efficient and scalable method for large-scale network partitioning on this neuromorphic system across different layers of hierarchy. The partitioning method results in a balanced assignment of neurons to each core in the network while providing flexibility to any input network structure. We demonstrate results on various input networks and compare different system router configurations in order to show the improved performance of the partitioning algorithm.

Mental stress appears in our daily life and affects our well-being and working performance. Previous studies have shown that mental stress, especially under chronic situations, can be related to obesity, heart disease, depression, or even suicide. Thus, mental stress has been recognized as the top one proxy killer. However, mental stress is not something we should necessarily avoid. When under adequate stress level, human's performance of cognitive task or sport are promoted. Therefore, it is essential to monitor stress level in daily life. This dissertation aims to build a brain-computer interface for online mental stress monitoring in the real world. We first investigated the Electroencephalography features reported in previous studies and found there are a lot of artifacts in data recorded in real-world scenarios. Hence, we moved on to investigate artifact removal methods and evaluate the performance of Artifact Subspace Reconstruction (ASR) using Independent Component Analysis (ICA). Next, we further evaluated the brain signal reconstruction ability of ASR and explored the human behaviors in a visual-oddball task conducted in a virtual environment. With all the results we obtained, we implemented ASR into our stress detection algorithms, and proposed an IC projection method to remove eye activities in real-time without performing ICA. Finally, to reduce the manufacturing cost and setup time of deployment, we investigated the effect of recording channels reduction to our stress detection algorithms. We found our stress detection algorithm with Linear Discriminant Analysis (LDA) can reach a 77\% balanced accuracy in an online scenario with only 11 recording channels placed in the frontal region.