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基于闪存的内存计算架构来实现高精度的稀疏编码
Yueran Qi1, Yang Feng1, Hai Wang1
1School of Information Science and Engineering, Shandong University, Qingdao 266237, China.
Micromachines
|December 23, 2023
概括
本研究介绍了一种基于Flash的新型内存计算 (CIM) 架构,用于高效的大规模稀疏编码. 拟议的变化敏感训练 (VST) 算法能够准确的图像重建,尽管硬件变化.
科学领域:
- 计算机工程 计算机工程
- 材料科学 材料科学 材料科学
- 数据科学数据科学数据科学
背景情况:
- 稀疏编码对于大数据处理至关重要,但在电力消耗和效率方面面临挑战.
- 计算在内存 (CIM) 架构通过将计算与内存集成,提供了一个有前途的解决方案.
- 基于闪存的CIM架构对于大规模应用特别有吸引力.
研究的目的:
- 为大规模稀疏编码提出一个新的基于Flash的CIM架构.
- 开发和优化一个变化敏感训练 (VST) 算法,以提高效率和准确性.
- 为了证明拟议的架构的可行性,用于高精度的图像重建.
主要方法:
- 实现了一种基于Flash的新型CIM架构.
- 验证各种矩阵式权力训练算法.
- 变化敏感训练 (VST) 算法的设计和优化,包括映射方法和初始化条件.
- 考虑数组变化的全面表征.
主要成果:
- 拟议的基于Flash的CIM架构成功实现了大规模的稀疏编码.
- 在图像重建应用中,VST算法提高了处理效率和准确性.
- 图像重建是在55nm闪存阵列中的训练字典中实现的,无论当前的变化如何.
结论:
- 基于Flash的CIM架构可用于实现高精度稀疏编码.
- 拟议的架构和VST算法解决了大数据处理中的功率和效率问题.
- 这项工作为使用CIM技术在各种应用中进行高效的稀疏编码铺平了道路.
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