I implemented YOLO26n inference from scratch in ARM64 Assembly + C on Raspberry Pi 4
Hi everyone,
I implemented the inference stage of YOLO26n completely from scratch using ARM64 Assembly and C, targeting the Raspberry Pi 4 (ARM Cortex-A72). The goal was not just to run a neural network, but to understand and reproduce many of the low-level optimizations used in modern ARM inference engines.
The project includes:
ARM NEON SIMD vectorization
Winograd Transform F(2×3) convolution
Optimized GEMM kernels
Cache-aware tiling
Custom ARM64 micro-kernels
Operator fusion
Pointwise and depthwise convolutions
Float32 inference pipeline
Implementation of YOLO26 components such as Conv, C3K2, SPPF, C2PSA, PSA, BottleNeck, and Detect
I also benchmarked different kernel designs and optimization strategies on Raspberry Pi 4 and compared their performance.
Repository:
https://github.com/mohammad-ghaderi/YOLO26
While the implementation works correctly and produces good detection results, the performance is still lower than I expected.
If you have any ideas, suggestions, or questions, I’d really appreciate hearing them.
Thanks for taking a look!