Whether it’s the AI-enabled system powering an autonomous car or making a financial transaction, or the AI tools making chip design decisions — all these parameters will require us to trust that these decisions will lead to better outcomes and productivity, rather than causing major specification defects, program delays, or financial implications for the customer. This will make companies prioritize different levels of trust within the hardware infrastructure that lies beneath to create secure channels for remote device management, service deployment, and lifecycle management (thus, ensuring that the entire system stack is trustworthy for the end-customer and not just the software).
As AI becomes more prevalent in computing applications, so too will the need for advanced trust and security at all levels of the system, especially at the design and integration stages. Up until now, AI hardware was not seen as critical in comparison to software. But with trust chains becoming especially important in the current environment of supply chain issues, companies will need a trusted chain throughout the workflow.
Ultimately, all these predictions will be driven by the need for faster computations, more intelligence at the edge, processing larger data volumes effectively, and automating more functions in the products we use. As AI permeates the enterprise, bold new hardware architectures and well-defined AI strategies will become a core enabler of innovation and seamless integration of AI into software systems. At Synopsys, we are committed to making technology smarter and more secure, from silicon to software, and to continue investing in accelerating the growth of disruptive AI-driven design solutions in the years to come.