What is Design Space Optimization?

Today, AI can interact with humans through natural language, identify bank fraud and protect computer networks, drive cars around city streets, and play intelligent games like chess and Go. Chip design is a very large space of potential solutions, trillions of times larger than, for example, the game of Go.

Searching this vast space is a very labor-intensive effort, typically requiring many weeks of experimentation, and often guided by past experiences and tribal knowledge. To create an optimal design recipe, engineers must ingest volumes of high-velocity data and make complex decisions on the fly with incomplete analysis, often leading to decision fatigue and over-constraining of their design. This manual, iterative, Design Space Exploration (DSE) process has partitioned the problem to manage its complexity. By consequence, the exploration of choices in design workflows has been severely limited, and designers’ productivity has been impacted by a large volume of small decisions.

In today’s hypercompetitive markets and stringent silicon manufacturing requirements, the difference between a good recipe and an optimal recipe can be 100s of MHz of performance; hours of battery life; or millions of dollars in design costs.

DSO massively scales the exploration of choices in chip design workflows, while automating a high volume of less consequential decisions.

The DSO Agent analyzes large data streams generated by design tools and uses them to make optimization decisions, observing how a design evolves over time and adjusting design choices, technology parameters, and workflows to guide the search process towards multi-dimensional optimization objectives. Searches can be executed at massive scale, with RL-driven decision engines autonomously operating tens-to-thousands of exploration vectors and ingesting gigabytes of high-velocity design analysis data – all in real-time.