Synopsys’s Amazon Deal and GPT-Synopsys: Ravi Subramanian
Notas do episódio
The demand for workload-specific hardware is forcing a strategic shift in the semiconductor IP business. Synopsys's Ravi Subramanian joins Austin to discuss why the company is moving to a new royalty-based model for its custom "application optimized IP," starting with a billion-dollar deal with Amazon. Ravi also unpacks Synopsys's partnership with OpenAI and its vision for AI "agent engineers" that augment human designers, while explaining why physics-based simulation remains the ultimate ground truth.
This episode is presented by Crusoe. Try Crusoe's managed inference for open models with $5 in free credits: https://semidoped.com/crusoe
"in physics we trust is because at the end of the day, the ground truth to have the confidence, not hallucinate and be deterministic in your outcome is really what defines is a product going to be successful or not."
— Ravi Subramanian, Synopsys
Key Takeaways:
- The hyperscaler IP model is shifting. Because 1-2 applications drive most revenue, companies like Amazon now demand custom “application optimized IP” instead of standards-based IP.
- Synopsys is adopting a new business model for this custom work: a license fee, a “customization fee” to prioritize scarce engineers, and for the first time, a royalty on the final chip.
- This new royalty model mirrors the Arm-Amazon partnership from a decade ago, aligning Synopsys with its customer’s 12-month design cycles and expanding its role to system-level bring-up.
- Synopsys’s AI strategy isn't just tools, but a new consumption model: customers can “hire” an AI agent engineer with a specific skillset, fitting directly into existing budgets and team structures.
- The GPT-Synopsys partnership with OpenAI is a turnkey service bundling a specialized LLM, the necessary compute, and forward-deployed engineers to solve the key adoption hurdles of model quality and compute scarcity.
- AI is for exploration, but physics is for verification. Accurately modeling a modern CFET requires 200x more device equations than a planar FET, a complexity that demands physics-based simulators as the ground truth.
Chapters:
0:00 Intro: The Amazon & OpenAI Deals
2:39 Application Optimized IP
6:04 The New Royalty Business Model
12:14 How Standard IP Differentiates
18:20 The Arm-Amazon Analogy
22:00 Synopsys's Role in Test Chips
26:06 The Three Stages of Chip Design
31:27 The 'Agent Engineer' AI Model
36:20 The GPT-Synopsys Partnership
39:27 In AI We Believe, In Physics We Trust
Follow Chipstrat:
Newsletter: https://www.chipstrat.com
X: https://x.com/chipstrat
Follow Vik:
Newsletter: https://www.viksnewsletter.com/
X: https://x.com/vikramskr
Follow Semi Doped:
Get more of Austin and Vik daily, free: https://daily.semidoped.com/