EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
Hacker News·Aug 24·5 min read
Hot Chips 2026: Applying High Bandwidth Flash (HBF)
TL;DR
High Bandwidth Flash (HBF) integrates SSD-like flash memory with compute chips to enhance memory capacity for machine learning workloads. While promising, it requires specialized software strategies for effective use.
✦ Why It Matters
Explore integrating HBF into your machine learning workflows to enhance memory capacity and reduce communication overhead.
Key Takeaways
- Explore HBF for high-capacity memory needs in machine learning.
- Consider software adaptations necessary for HBF integration.
- Evaluate workload characteristics to assess HBF cost-effectiveness.
- Investigate existing SSD streaming techniques for potential HBF applications.
How It Works
HBF integrates flash memory directly onto compute chips, allowing for higher capacity than HBM while maintaining reasonable bandwidth. Data transfers between HBF and DRAM utilize Direct Memory Access (DMA), requiring large, aligned chunks for efficient access, similar to block storage devices.
⚠ The Catch
HBF's unique access requirements complicate software adaptation, making it less user-friendly than traditional SSDs. Software must manage data in large blocks, which can hinder performance if not optimized correctly.