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Estimating neural networks-based algorithm for adaptive cache replacement
1Dept. of Comput. Sci., Monmouth Univ., West Long Branch, NJ.
Summary
This study introduces a novel adaptive cache replacement strategy using neural networks (NNs) to predict and remove inactive cache lines. This approach significantly enhances cache performance by prioritizing actively used data.
Area of Science:
- Computer Science
- Artificial Intelligence
- Computer Architecture
Background:
- Cache memory is crucial for system performance, but traditional replacement policies struggle with dynamic workloads.
- Inefficient cache replacement leads to frequent cache misses and reduced data access speed.
Purpose of the Study:
- To develop an adaptive cache replacement scheme utilizing neural networks (NNs).
- To improve cache performance by effectively identifying and eliminating inactive cache lines.
Main Methods:
- Employing the statistical prediction capabilities of estimating neural networks (NNs).
- Developing a neural network-based replacement policy to manage cache lines.
- Simulating the proposed scheme across various cache configurations.
Main Results:
- The proposed neural network-based replacement scheme effectively identifies and removes inactive cache lines.
- Demonstrated significant performance advantages over conventional cache replacement policies.
- Achieved larger free space within the cache for actively referenced data.
Conclusions:
- Adaptive cache replacement using neural networks offers a substantial performance improvement.
- The strategy effectively balances cache space for frequently accessed data.
- This approach represents a promising advancement in cache management techniques.
