Golam Rabby

1. Implemented and benchmarked DL models (AlexNet, LeNet-5, ResNet, MobileNet, VGG-16) on PyTorch, comparing nvcc/OpenXLA compilers with CIFAR-10/MNIST. 2. Evaluated accuracy, training/inference times, and throughput on single/multi-GPUs. Optimized TinyML models on Nordic MCUs (TF Lite Micro, microTVM, PyTorch Mobile) for memory and on-device performance. 3. Applied quantization, pruning, and NAS for CNN compression. 4. Analyzed Apache TVM (UMA, BYOC, microTVM, ZigZag) for IoT/Edge hardware.

1. Implemented two functions to erase both boot and root partition using Kernel error handling API's, to mitigate filesystem corruption. 2. Developed a software service from a communication protocol named Unified Diagnostic Services namely ReadDataByIdentifier, which task is to provide the latest Linux version number to the client. 3. Developed a softcore FPGA IP core using VHDL to extract 16-bit DAC (Digital to Analog Converter) signals and store those signals in FPGA's internal BRAM memory.

|| Embedded Machine Learning Developer || C, C++, Python and TinyML Enthusiast.