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

2026-07-26 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Internet of Things (IoT) & Connected Devices · Depth: Expert, quick

Summary

A Bachelor's Final Project involved implementing YOLO26n model inference entirely from scratch using ARM64 Assembly Language and C, bypassing existing inference frameworks. The primary objective was to gain a deep understanding of modern neural network inference engines at a low level and explore optimization techniques for faster, more efficient edge AI execution on a Raspberry Pi 4. The comprehensive implementation incorporated ARM64 Assembly Language and C for the inference engine, ARM NEON SIMD optimization, Winograd convolution, optimized GEMM kernels, cache-aware tiling, custom ARM64 micro-kernels, and operator fusion. It also included the attention mechanism and specific YOLO26 components like Conv, C3K2, SPPF, C2PSA, PSA, BottleNeck, and Detect. The author extracted YOLO26n model parameters and redesigned the memory layout into a custom binary format. While the implementation correctly performs object detection, the achieved performance improvement was less than anticipated, prompting a request for feedback on CNN inference, ARM NEON, memory/cache optimization, and low-level neural network acceleration.

Key takeaway

For AI Engineers optimizing edge device performance, directly implementing neural network inference in ARM64 Assembly and C, as demonstrated with YOLO26n on Raspberry Pi 4, offers profound insights into low-level execution. You should consider custom memory layouts and Winograd convolution for 3x3 kernels to potentially improve efficiency. Be prepared that significant performance gains may require extensive optimization, and community feedback can be crucial for identifying further acceleration avenues.

Key insights

Implementing neural network inference from scratch reveals low-level optimization opportunities for edge AI, though performance gains can be challenging.

Principles

• Low-level implementation deepens NN engine understanding.
• Custom memory layouts optimize inference pipelines.
• Winograd convolution excels for kernelSize=3.

Method

Implement YOLO26n inference using ARM64 Assembly and C, incorporating ARM NEON SIMD, Winograd convolution, optimized GEMM, cache-aware tiling, custom micro-kernels, and operator fusion. Redesign memory layout for custom binary format.

In practice

• Explore ARM NEON for vectorization.
• Test Winograd for 3x3 convolutions.
• Optimize memory layout for cache hits.

Topics

• YOLO26n
• ARM64 Assembly
• Neural Network Inference
• Edge AI Optimization
• Raspberry Pi 4
• Winograd Convolution
• SIMD Optimization

Code references

• mohammad-ghaderi/YOLO26

Best for: Computer Vision Engineer, AI Scientist, Research Scientist, AI Engineer, Machine Learning Engineer, AI Hardware Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.