# I Developed the YOLO26n Model from Scratch Using ARM64 Assembly Explored optimization techniques for edge AI on Raspberry Pi 4. - **Source:** [r/MachineLearning](https://www.reddit.com/r/MachineLearning) - **Published:** 2026-07-26 - **Topic:** [Data & AI](https://brifing.si/c/data-ai) - **Original article:** - **On Brifing:** https://brifing.si/s/dab39ada-76ac-488d-aa0d-2486b21f6cb0 ## Project Goal This project aims to understand how modern neural network inference engines work at a low level and explore optimization techniques for faster and more efficient AI execution on edge devices like Raspberry Pi 4. ## Technical Details The project was developed using ARM64 Assembly language and C, equipped with techniques like ARM NEON SIMD optimization, Winograd convolution, and custom GEMM kernels. ## Memory Layout YOLO26n model parameters were extracted and converted into a custom binary format optimized for the inference pipeline. ## Operator Fusion Operator fusion and custom ARM64 micro-kernels were used to enhance performance. ## Results The project produced correct object detection results but did not fully achieve the expected performance improvement. ## Feedback The project is open to feedback and suggestions on CNN inference optimization, ARM NEON/vectorization, and memory layout. ## Sources covering this story - https://www.reddit.com/r/MachineLearning/comments/1v6w394/i_implemented_the_yolo26n_model_inference_from/ — --- _Summary written by Brifing from the original reporting. Copyright in the original article belongs to its publisher; read the full piece at the link above._