EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
Hacker News·Aug 21·5 min read
A look under our trunk: what's in our compute of Waymo taxis
TL;DR
Waymo has developed a cutting-edge compute system for its autonomous taxis, designed for real-time driving with ultra-low latency and high reliability. This system integrates custom silicon and advanced machine learning to ensure safe, efficient operation on the road.
✦ Why It Matters
Evaluate your own systems for real-time processing capabilities and consider implementing redundancy for safety.
Key Takeaways
- Investigate custom silicon solutions for your projects.
- Consider low-latency requirements in autonomous systems.
- Explore partnerships with industry leaders for technology scaling.
- Focus on rugged hardware design for real-world applications.
How It Works
Waymo's compute system processes sensor data using a highly integrated architecture that combines CPUs, GPUs, and specialized ASICs. The ASIC is designed to handle complex machine learning tasks, enabling real-time sensor fusion and decision-making.
This architecture allows for efficient data processing while maintaining low latency, crucial for safe autonomous driving.