Building an AI Chip: Pre Silicon Planning

This white paper highlights the challenges of AI chip design, including balancing performance, cost, and power efficiency. It emphasizes the importance of early architecture exploration to avoid costly design revisions and ensure optimal power-performance trade-offs. The paper underscores the need for secure, efficient, and scalable IP solutions to meet the evolving demands of AI applications, ensuring successful and timely delivery of AI chips.

AI has become a dominant consumer of computational power, necessitating specialized hardware for efficient processing. Designing AI chips is complex, requiring specialized knowledge and tools at every stage, particularly in pre-silicon planning. 

The AI market is rapidly growing, with machine learning and deep learning leading to generative AI, which has vast potential applications. AI workloads demand larger, faster, and more power-hungry silicon architectures, driving innovation in chip design.