Field-Programmable Gate Arrays (FPGAs) are relatively high-end devices that are not easily shared between multiple users. In this work, we achieved a remotely accessible FPGA framework using accessible Internet of Things (IoT) approaches. We sought to develop a method for students to receive the same level of educational quality in a remote environment that they would receive in a typical, in-person course structure for a university-level digital design course. Keeping cost in mind, we are able to combine the functionality of an entry-level FPGA and a Raspberry Pi Zero to provide IoT access for laboratory work. Previous works in this field allow only one user to access an FPGA at a time, which requires students to schedule time slots. Our design is unique in that it gives multiple users the ability to interact simultaneously with one individual top-level design on an FPGA. This novel design has the benefit for classroom presentations, collaboration and debugging, and eliminates the need for restricting student access to a time slot for FPGA access. Further, our hardware wrapper is lightweight, utilizing less than 1% of tested FPGA chips, allowing it to be integrated with resource-heavy designs. The application is meant to scale with large designs; there is no difference between how many users can interact with the remote design, regardless of the complexity of the design. Further, the number of users who can interact with a single project is limited only by the bandwidth restrictions imposed by Google Fire Base, which is far beyond any practical number of users for simultaneous access.
Modern networks used for integrating custom Internet of Things (IoT) systems and devices have restrictions and requirements unique to their individual applications. These application specific demands require custom designed hardware to maximize throughput, security and data integrity whilst minimizing latency, power consumption, and form factor. Within this paper, we describe current, state-of-the-art works that utilize FPGAs for IoT network developments. We analyze two categories of works: those that prioritize reducing power consumption, and those that prioritize networking features. Further, we describe how future works can improve upon these designs and therefore improve the efficiency of resource-constrained IoT networks.
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