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Erwan Régy  • Peerlist

This project entailed creating an Arm (previously RISC-V) skydiving altimeter SoC using the AMS 0.35 μm process, all the way from specification to fabrication.

Arm Skydiving Altimeter System-on-Chip

This project involved the creation of an inertial motion capture system. It employed unique, custom-built flexible sensors designed for efficient and precise tracking of human movements rendered onto a 3D avatar. These compact, battery-operated sensors boasted minimal latency and low power usage while transmitting data wirelessly to a host computer. The host computer, leveraging Unity, displayed this data graphically and filtered it into quaternions, which were then applied to a 3D avatar model for realistic motion portrayal.

Human Motion Capture System and On-Screen Ava

This project explored the implementation of a neural processing unit (NPU) on a field-programmable gate arrays (FPGA) to enhance convolutional neural network (CNN) inference performance for handwritten digit recognition using the MNIST dataset. The project employed a modular and scalable architecture based on LeNet-5, developed in SystemVerilog, alongside a custom software implementation used for training developed in C++. A pre-trained Python TensorFlow model additionally served as a reference for benchmarking. Promising results achieved underscored the potential of FPGA-based NPUs for accelerating CNN inference in real-world applications and contributing to the expanding research in FPGA-based deep learning accelerators.