I Developed the YOLO26n Model from Scratch Using ARM64 Assembly
Explored optimization techniques for edge AI on Raspberry Pi 4.
1Project 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.
2Technical 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.
3Memory Layout
YOLO26n model parameters were extracted and converted into a custom binary format optimized for the inference pipeline.
4Operator Fusion
Operator fusion and custom ARM64 micro-kernels were used to enhance performance.
5Results
The project produced correct object detection results but did not fully achieve the expected performance improvement.
6Feedback
The project is open to feedback and suggestions on CNN inference optimization, ARM NEON/vectorization, and memory layout.
Original article: r/MachineLearning — YOLO26n Modelini ARM64 Assembly ile Sıfırdan Geliştirdim