DAMP method optimizes recurrent states in language models

VERA Brief

AI-generated. Grounded in the article and its cited sources.

A new method called DAMP has been introduced to optimize recurrent states in language models. DAMP uses decay-aware mixed-precision quantization to address memory and latency issues without significantly impacting accuracy.

Key facts

• DAMP is a technique for optimizing recurrent states in language models.
• It addresses memory and latency issues in large language models.
• DAMP employs decay-aware mixed-precision quantization for recurrent states.
• The method stores high-risk channels at higher precision and others in INT8.
• Evaluations show DAMP maintains average accuracy at 9.9 bits per state value.