通过向前送脉冲方案,通过线性强化增强memristor多级电阻状态
Zhuo Diao1, Ryohei Yamamoto1, Zijie Meng1
1Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama-Cho, Toyonaka, Osaka 560-8531, Japan. diao.zhuo.es@osaka-u.ac.jp.
Nanoscale horizons
|February 13, 2025
概括
研究人员开发了一种用于模拟memristors的新型电阻控制方法,显著提高了精度,并实现了512个可编程级别. 这一进步提高了边缘设备上人工智能 (AI) 模型的准确性.
科学领域:
- 材料科学 材料科学 材料科学
- 计算机科学 计算机科学
- 电气工程 电气工程
背景情况:
- 人工智能 (AI) 应用受益于将人工神经网络 (ANN) 权重映射到模拟内存电阻,以提高吞吐量和能源效率.
- 在模拟记忆元上实施人工智能面临着由于非线性电阻切换和有限的数值比特精度造成的挑战,这影响了ANN模型的准确性.
研究的目的:
- 为模拟memristors引入一个精确的电阻控制方法.
- 增强可编程阻力级数量的数量,以改善人工智能计算.
- 评估多级电阻状态对ANN准确性的影响.
主要方法:
- 开发了一个前脉冲方案,以精确控制memristor电阻.
- 一个基于TiO2的memristor被用来演示电阻控制方法.
- 建立了一个评估框架,以评估在不同阻力状态下ANN的准确性.
主要成果:
- 该方法在基于TiO2的memristor上实现了512个可编程电阻状态,电阻比率低 (1.19).
- 在ResNet-34 (超过2000万个参数) 上通过使用模拟记忆器的重量转移证明了95.5%的准确性.
- 成功地将ANN重量映射到memristor电阻,展示了AI推断的潜力.
结论:
- 开发的电阻控制方法显著提高了对AI的模拟memristor精度和可编程性.
- 模拟记忆器显示出精确AI模型推断的巨大潜力,特别是在边缘计算应用中.
- 未来的工作可以专注于增加对更复杂的人工智能任务和内存计算能力的阻力状态.
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