The exponential growth in the scale and complexity of neural recording systems demands increasingly efficient computational frameworks to manage the vast data generated by modern multi-electrode ar- rays. This thesis explores the feasibility of integrating Graphics Processing Unit (GPU) acceleration with the Open Ephys ONIX system to enhance its support for next-generation neural interfaces and compu- tational algorithms. ONIX, a modular and open-source electrophysiology platform, currently relies on CPU-based computations that may struggle to meet the rising demands of closed-loop experiments. This work investigates PCIe peer-to-peer (P2P) communication between ONIX and GPUs to by- pass CPU involvement, aiming to reduce latency and increase throughput. A secondary data channel was implemented on the ONIX FPGA, accompanied by a custom kernel driver integrating GPUDirect with the existing RIFFA driver. Performance evaluations compared unpinned and pinned GPUDirect transfers to traditional CPU-mediated methods. Pinned transfers reduced transfer times by up to 30% for small data and 14% for larger data sizes, though significant variance in transfer time was observed. Application-level benchmarks revealed that GPU acceleration provides advantages for larger data sizes and higher computational loads but the effectiveness of GPUDirect is limited by ONIX’s PCIe through- put. The findings highlight that while GPUDirect offers theoretical benefits, its practical impact is con- strained by hardware limitations, including the PCIe bandwidth of the ONIX FPGA. This thesis out- lines necessary hardware improvements, such as higher-speed SerDes modules and advanced FPGA cards, to fully realize the potential of GPU acceleration in electrophysiology systems. A decision tree framework is proposed to guide researchers in determining scenarios where GPU and GPUDirect in- tegration is beneficial. While this study demonstrates the feasibility of GPU integration with ONIX, GPUDirect integration is currently not a beneficial technology for the ONIX ecosystem.

In this thesis, the design and implementation of a wireless communication module and an accompanying network are discussed. This wireless communication module is used in a device which communicates frost temperatures measured at orchards. The goal is to gather data on how fruit frost develops on orchards, and to warn a fruit farmer if it starts freezing. First, several wireless communication protocols are discussed and the one best suited for the application is chosen. It is decided to use the LoRaWAN communication protocol. Then, the off-the-shelf hardware components which are required to implement the LoRaWAN communication protocol are chosen. Furthermore, LoRa communication parameters and the LoRaWAN network structure are discussed. A scheduling system is designed and proposed to increase the reliability of the network. At last, network simulations are performed to verify the chosen implementation.