A system processes a very long sequence of data pairs, one at a time. At each step, it must update a 'memory state' to be the precise mathematical average of all data pairs seen up to that point. Consider two methods for updating the memory state at the 10,000th step:
Method A: Re-access the entire history of 10,000 data pairs, sum them up, and divide by 10,000.
Method B: Use only the memory state from the 9,999th step (which was the average of the first 9,999 pairs) and the new 10,000th data
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Ch.2 Generative Models - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
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Memory Efficiency of Recursive Cumulative Average
A system processes a very long sequence of data pairs, one at a time. At each step, it must update a 'memory state' to be the precise mathematical average of all data pairs seen up to that point. Consider two methods for updating the memory state at the 10,000th step:
Method A: Re-access the entire history of 10,000 data pairs, sum them up, and divide by 10,000.
Method B: Use only the memory state from the 9,999th step (which was the average of the first 9,999 pairs) and the new 10,000th data
Calculating a Recursive Memory State
Step-by-Step Memory State Calculation
Inference Efficiency of Cumulative Average Memory