I implemented the YOLO26n model inference from scratch using ARM64 Assembly Language (No framework) [P]

Short summary

A Bachelor's final project implements YOLO26n object detection inference entirely from scratch using ARM64 Assembly and C on a Raspberry Pi 4, with no external inference frameworks. The implementation includes NEON SIMD optimization, Winograd convolution, cache-aware tiling, operator fusion, and custom micro-kernels covering all YOLO26 components. The author reports correct detection results but lower-than-expected performance gains and seeks community feedback on CNN inference optimization and memory layout strategies.

• •YOLO26n inference engine built from scratch in ARM64 Assembly + C with no frameworks
• •Includes NEON SIMD, Winograd convolution, GEMM kernels, cache-aware tiling, and operator fusion on Raspberry Pi 4
• •Author seeks feedback on optimization techniques; performance gains were below expectations

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