The demand for implementing neural networks on edge devices has rapidly increased as they allow designers to move away from expensive server-grade hardware. However, due to the limited resources available on edge devices, it is challenging to implement complex neural networks. This study selected the Kria SoM KV260 hardware platform due to its affordability and sufficient hardware capabilities for creating a resource-constrained environment. By leveraging the hardware acceleration capabilities of the FPGA for specific nodes of the MobileNetv1 model and offloading other nodes to the onboard quad-core ARM cortex-A53 CPU, it was feasible to implement a neural network on a hybrid combination of CPU and FPGA. Results showed that when executing the MobileNetv1 model in a hybrid configuration, a total runtime improvement of 2.8x over a pure CPU implementation can be achieved. The study concludes that node-wise partitioning of the MobileNetv1 model is a practical solution. This approach offers a cost-effective solution for users who seek an accessible way to run neural networks without the need for expensive server-grade hardware.

This project is to design and implement a reconfigurable measurement interface for Internet of Things sensors, for the Microelectronics Department of the Delft University of Technology. This thesis will discuss the functionality and design process taken in designing such a reconfigurable measurement interface, focusing on generating signals and control control signals for the hard- ware. The important choices will be highlighted and the strengths and weaknesses of each design choice will be weighed in order to produce a fully functional measurement interface. In the span of 10 weeks this group was able to successfully generate sinusoidal, square, and triangular waves as well as DC voltages at +12V and +24V and in the voltage range of -10V to +10V on different pins of the dip-48 socket. Sensor output voltages can also be measured and observed using an external computer.