DigiTekXplorer - AI Case Studies

We showcase three projects developed in collaboration with AI, demonstrating its power to accelerate the design and implementation of complex systems. The projects span mobile application development, computer architecture, and FPGA design. These case studies highlight the capabilities of Google's Gemini 2.5 Pro in a practical engineering context.

1. B_Bot: BLE Android Control Application

I developed a custom Android application to wirelessly control the B-Bot rover via Bluetooth Low Energy (BLE). Using a combination of Android Studio and Google's Gemini Pro, I accelerated the development lifecycle.

Tools:

Android Studio 2024.3.1

Gemini 2.5 Pro

• AI Collaboration: Gemini was instrumental in generating the initial boilerplate code for Android's BLE services and characteristics. It also helped debug complex asynchronous callbacks and provided alternative implementations for the user interface layout in XML, allowing for rapid prototyping.

2. abCore16: Custom 16-bit Microprocessor Ecosystem

The abCore16 project is a complete 16-bit microprocessor ecosystem written entirely in Python. It features a custom CPU architecture and a comprehensive software development toolchain built within a PyCharm environment.

The ecosystem enables software development for the abCore16 CPU through two distinct compilation paths:

1. A simple, direct-to-assembly language.
2. A more powerful, C-like high-level language (SSL). This language supports modern programming constructs including functions with local variables, if/else, for, and while loops, and global one-dimensional arrays.

The toolchain provides a seamless workflow from high-level code to execution:

• A PLY-based C-like Compiler parses the source, builds an Abstract Syntax Tree (AST), and generates assembly code.
• An Assembler converts the assembly code into 16-bit binary machine code.
• A Simulator executes the binary code on a detailed model of the abCore16 CPU, providing cycle-by-cycle logging and state inspection.
• A Disassembler can reverse-engineer the binary back into human-readable assembly for verification and debugging.

The project serves as a practical, hands-on platform for exploring and implementing concepts in computer architecture and compiler design.

Tools:

PyCharm 2025.1.1

Gemini 2.5 Pro

• AI Collaboration: This project was a deep partnership with Gemini Pro. I used it as an architectural consultant to define the instruction set and addressing modes. The AI then generated foundational Python code for the assembler and the CPU's fetch-decode-execute cycle in the simulator, which I then integrated and refined.

3. abUART: FPGA-Based UART and Testbench in SystemVerilog

Yes, Gemini 2.5 Pro can generate Hardware Description Language (HDL) code for FPGA implementation. Currently, there is a huge demand for FPGA developers so the ability to design systems for FPGA implementation is a practical as well as incredibly useful skill.

I designed, implemented, and verified a Universal Asynchronous Receiver/Transmitter (UART) module for FPGAs using SystemVerilog.  The UART was verified in Vivado’s simulation environment.  The design was taken from design entry all the way to bitfile generation for a Digilent Arty S7-50 FPGA development board.

Tools:

AMD (Xilinx) Vivado 2024.2

Gemini 2.5 Pro

• AI Collaboration: My workflow involved using Gemini to translate high-level requirements into a functional design. The AI generated the initial SystemVerilog code for the receiver/transmitter state machines and the baud rate generator.  Subsequently, it created a comprehensive testbench to verify the module's functionality, drastically reducing the time required for verification.

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