NAC: A Universal Instruction Set Architecture and Compiler for AI, Standardizing Neural Networks into Analyzable 'Genomes'

The NAC (Neural Architecture Compiler) project was developed by FekDN and released on GitHub on May 26, 2026. This project proposes a universal instruction set architecture (ISA) and compiler system for AI, with its core innovation being the standardization of neural networks into a "genome" format. It aims to solve the challenges of deploying, optimizing, and adapting complex models to hardware, enabling in-depth analysis, optimization, and hardware synthesis.

With the rapid development of AI technology, the complexity of neural network models has increased dramatically (e.g., large language models with hundreds of billions of parameters), posing huge challenges to deployment, optimization, and hardware adaptation. While traditional deep learning frameworks (PyTorch, TensorFlow) provide rich training functions, they have many limitations in model deployment and hardware optimization. Thus, the NAC project was born, attempting to fundamentally solve portability and optimization issues by defining a universal ISA to abstract neural networks into a standardized "genome" representation.

The core design philosophy of NAC is "Neural Network as Genome": the genome contains all information about network structure, connection methods, and computational logic, analogous to the genetic information of biological genomes. This representation has three major advantages: standardization (eliminating framework barriers), deep analyzability (identifying redundant computations and parallelization opportunities), and hardware independence (cross-platform deployment).

The compiler workflow consists of four stages: front-end (importing and parsing models from frameworks like PyTorch/TensorFlow), intermediate representation (genome encoding, including topological structure and computational attributes), optimization layer (advanced optimizations such as operator fusion and constant folding), and back-end (generating code for GPU/CPU/specialized accelerators/FPGA).

The application scenarios of NAC include:

1. Cross-platform deployment: Train once, deploy anywhere, reducing the workload of model porting;
2. Hardware co-design: Chip designers can analyze workload characteristics to optimize architectures;
3. Model compression and acceleration: Discover optimization opportunities that are hard to identify with traditional methods (e.g., removing redundant computation nodes);
4. Research and education: The standardized representation facilitates analysis and comparison of network structures, helping to understand principles.

Current challenges:

1. Ecosystem compatibility: Need to enrich operator support and optimization strategies to compete with mature frameworks;
2. Dynamic network support: Focuses on static networks, with incomplete support for dynamic graphs and variable structures;
3. Debugging and observability: Design issues in maintaining correspondence with the original model.

Future directions:

• Expand support for emerging architectures such as generative AI;
• Strengthen cooperation with hardware vendors to optimize accelerator support;
• Develop genome visualization tools;
• Build an open ecosystem to attract community participation.

The NAC project is an important exploration in the field of AI infrastructure. Through genome standardization, it provides new ideas for solving core issues such as model portability, optimization, and hardware adaptation. Although it is in the early stage, its design philosophy and technical route are worth continuing to pay attention to. For engineers and researchers in AI deployment, compiler development, or hardware design, it is a reference implementation worth in-depth study.