<p>➀ The rise of edge AI has spurred semiconductor designers to build accelerators for performance and low power, leading to a proliferation of NPUs among in-house, startup, and commercial IP product portfolios.</p><p>➁ The complexity of software and hardware around neural network architectures, AI models, and base models is exploding, requiring sophisticated software compilers and instruction set simulators.</p><p>➂ The hardware complexity of inference platforms is evolving, with a focus on performance and power efficiency, especially for edge applications.</p><p>➃ The combination of tensor engines, vector engines, and scalar engines in multiple clusters to address the challenges of acceleration is complex and costly.</p><p>➄ The supply chain and ecosystem for NPUs are becoming increasingly complex, with intermediate manufacturers and software companies having limited resources to support a wide range of platforms.</p>