SMART-WRITE: Adaptive Learning-based Write Energy Optimization for Phase Change Memory
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| Format: | Preprint |
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2025
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| _version_ | 1866914141990551552 |
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| author | Desai, Mahek Quinn, Rowena Asadinia, Marjan |
| author_facet | Desai, Mahek Quinn, Rowena Asadinia, Marjan |
| contents | As dynamic random access memory (DRAM) and other current transistor-based memories approach their scalability limits, the search for alternative storage methods becomes increasingly urgent. Phase-change memory (PCM) emerges as a promising candidate due to its scalability, fast access time, and zero leakage power compared to many existing memory technologies. However, PCM has significant drawbacks that currently hinder its viability as a replacement. PCM cells suffer from a limited lifespan because write operations degrade the physical material, and these operations consume a considerable amount of energy. For PCM to be a practical option for data storage-which involves frequent write operations-its cell endurance must be enhanced, and write energy must be reduced. In this paper, we propose SMART-WRITE, a method that integrates neural networks (NN) and reinforcement learning (RL) to dynamically optimize write energy and improve performance. The NN model monitors real-time operating conditions and device characteristics to determine optimal write parameters, while the RL model dynamically adjusts these parameters to further optimize PCM's energy consumption. By continuously adjusting PCM write parameters based on real-time system conditions, SMART-WRITE reduces write energy consumption by up to 63% and improves performance by up to 51% compared to the baseline and previous models. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_04713 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SMART-WRITE: Adaptive Learning-based Write Energy Optimization for Phase Change Memory Desai, Mahek Quinn, Rowena Asadinia, Marjan Hardware Architecture Emerging Technologies As dynamic random access memory (DRAM) and other current transistor-based memories approach their scalability limits, the search for alternative storage methods becomes increasingly urgent. Phase-change memory (PCM) emerges as a promising candidate due to its scalability, fast access time, and zero leakage power compared to many existing memory technologies. However, PCM has significant drawbacks that currently hinder its viability as a replacement. PCM cells suffer from a limited lifespan because write operations degrade the physical material, and these operations consume a considerable amount of energy. For PCM to be a practical option for data storage-which involves frequent write operations-its cell endurance must be enhanced, and write energy must be reduced. In this paper, we propose SMART-WRITE, a method that integrates neural networks (NN) and reinforcement learning (RL) to dynamically optimize write energy and improve performance. The NN model monitors real-time operating conditions and device characteristics to determine optimal write parameters, while the RL model dynamically adjusts these parameters to further optimize PCM's energy consumption. By continuously adjusting PCM write parameters based on real-time system conditions, SMART-WRITE reduces write energy consumption by up to 63% and improves performance by up to 51% compared to the baseline and previous models. |
| title | SMART-WRITE: Adaptive Learning-based Write Energy Optimization for Phase Change Memory |
| topic | Hardware Architecture Emerging Technologies |
| url | https://arxiv.org/abs/2511.04713 |