Panel with Arteris, GlobalFoundries, Tenstorrent: RISC-V Ecosystem Growth for Physical AI

2026-06-29 · Source: Big Data & AI News - EE Times · Field: Technology & Digital — Robotics & Autonomous Systems, Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation · Depth: Advanced, quick

Summary

A panel discussion featuring Arteris, GlobalFoundries, and Tenstorrent explored the growth of the RISC-V ecosystem for physical AI. Key themes included edge autonomy and optimizing for TOPS-per-watt, with a focus on how these advancements drive the development of a "killer app" for robots.

Key takeaway

For AI Hardware Engineers designing edge systems, prioritize RISC-V architectures to achieve optimal TOPS-per-watt efficiency. Your designs must support physical AI and edge autonomy, especially for robotics. Focus on integrating RISC-V IP to enable the next generation of intelligent, power-constrained devices.

Key insights

RISC-V is a key enabler for physical AI and edge autonomy, driven by TOPS-per-watt efficiency.

Principles

• RISC-V ecosystem is expanding for AI.
• TOPS-per-watt is critical for edge AI.

In practice

• Develop AI for edge autonomy.
• Optimize for high TOPS-per-watt.
• Target robotic "killer apps".

Topics

• RISC-V Ecosystem
• Physical AI
• Edge Autonomy
• TOPS-per-watt
• Robotics AI

Best for: AI Hardware Engineer, Robotics Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by Big Data & AI News - EE Times.