PRISM: Breaking the O(n) Memory Wall in Long-Context LLM Inference via O(1) Photonic Block Selection

1. 1Identify the memory bottleneck in long-context LLM inference
2. 2Apply photonic accelerators for dense attention computation
3. 3Implement O(1) photonic block selection to reduce memory scaling
4. 4Evaluate the performance of PRISM in various LLM inference scenarios

Machine learning researchers and engineers working on large language models can benefit from this research to improve inference efficiency, and software engineers can apply the photonic block selection technique to optimize memory usage

💡 Photonic block selection can reduce memory scaling in long-context LLM inference

💡 PRISM breaks O(n) memory wall in LLM inference with O(1) photonic block selection!