WIA-AI-011: AI Chip Interface

Goal: Define unified tensor formats, memory layouts, and data types across all AI accelerators.

Key Features:

• Tensor descriptor format (shape, stride, dtype)
• Memory layout standards (NCHW, NHWC, etc.)
• Quantization format specifications (INT8, FP16, BF16)
• Zero-copy buffer sharing protocols

Goal: Create hardware-agnostic APIs for accelerator operations.

Key Features:

• Device discovery and enumeration
• Context and stream management
• Kernel launch abstractions
• Unified memory allocation interface

Goal: Establish protocols for inter-chip communication and synchronization.

Key Features:

• Multi-chip synchronization primitives
• DMA transfer protocols
• Event-based coordination
• Distributed execution framework

Goal: Optimize performance and integrate with existing AI frameworks.

Key Features:

• Auto-tuning and kernel optimization
• Framework integration (PyTorch, TensorFlow, JAX)
• Performance profiling tools
• Compiler optimization passes