Hardware-Software Co-Design for edge AI is the joint optimization of model architecture and hardware accelerator design — designing the model to exploit hardware capabilities (parallelism, memory hierarchy) and the hardware to efficiently execute the target model workload.

Co-Design Dimensions

• Model → Hardware: Design custom hardware (NPU, ASIC) optimized for a specific model architecture.
• Hardware → Model: Design model architectures that map efficiently to existing hardware (GPU, MCU, FPGA).
• Joint: Simultaneously search the model architecture and hardware configuration space.
• Compiler: Hardware-aware compilers (TVM, MLIR) bridge the gap between model and hardware.

Why It Matters

• Efficiency: Co-designed systems achieve 10-100× better energy efficiency than generic hardware running generic models.
• Edge Constraints: Edge devices have strict power, area, and cost budgets — co-design is essential.
• Semiconductor: Chip companies can co-design AI accelerators with target AI models for maximum performance per watt.

Co-Design is optimizing both sides together — jointly designing the model and hardware for maximum edge AI performance and efficiency.