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Model-Based Approach for Developing Optimal HW/SW Architectures for AI systems

Artificial Intelligence (AI) algorithms in a real-time operating environment require huge amounts of computation resources and process gigabytes of data. An efficient AI system needs carefully analyzed Hardware/Software partitioning and architecture that can meet real-time processing, energy efficiency, and cost requirements. Using a Model-Based Cybertronic Systems Engineering (MBCSE) methodology the design complexity and challenging requirements can be tackled.

This tutorial will demonstrate the design process as a transformation of an AI algorithm from a Python program to an efficient, low power implementation with a mix of hardware and software elements. The design process applies Architecture Analysis & Design Integrated Approach (Arcadia), SystemC-based HW/SW co-architecting and High-Level Synthesis (HLS) methodologies.