EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
Hacker News·Aug 2·5 min read
Persistent State Machines: LLM Attention with INT4 In-Memory Cells
TL;DR
A formal framework for attention operators in Large Language Models (LLMs) using Persistent State Machines (PSMs) was developed, demonstrating low power consumption in hardware implementations. The architecture achieved dynamic energy efficiency below 1.0 mW with a normalized dynamic energy of 3.81 × 10^-5 pJ/op.
✦ Why It Matters
Consider implementing Persistent State Machines for energy-efficient LLM applications in your next hardware project.
Key Takeaways
- Explore PSMs for efficient attention mechanisms in LLMs.
- Utilize low-power FPGA designs to reduce energy consumption.
- Implement system-on-chip designs for better integration.
- Validate designs with functional simulations for accuracy.
How It Works
PSMs operate by broadcasting computation to stationary in-memory cells that evaluate state transitions deterministically. This allows for efficient attention mechanisms in LLMs, minimizing power usage while maintaining performance.