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A Method for Growing Bio-memristors from Slime Mold
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适应磁还原性内存设备,以实现准确且无需芯片内训练的内存计算
Zhihua Xiao1,2, Vinayak Bharat Naik3, Jia Hao Lim3
1The Hong Kong University of Science and Technology, Hong Kong, China.
Science advances
|September 18, 2024
概括
本研究介绍了使用磁道连接 (MTJ) 进行模拟内存计算 (AiMC) 的硬件软件方法. 它在不需要芯片上训练的情况下实现了高精度,克服了人工智能任务的memristor限制.
科学领域:
- 材料科学:专注于磁道连接 (MTJ) 设备.
- 计算机工程:探索模拟内存计算 (AiMC) 架构.
- 人工智能:研究深度神经网络 (DNN) 的加速.
背景情况:
- 记忆器是AiMC交叉阵列中高效的多重积累 (MAC) 操作的关键.
- 记忆元和电路中的设备变化阻碍了模拟计算的准确性.
- 在芯片上训练,一个常见的解决方案,是受限于memristor耐力.
研究的目的:
- 为基于MTJ的AiMC开发一个硬件软件代码设计.
- 为了实现软件级准确度,而不需要芯片上训练.
- 为了利用MTJ的低可变性进行准确的AI推断.
主要方法:
- 使用的磁道连接 (MTJ) 设备用于AiMC.
- 实施了基于MTJ的AiMC的离芯片校准方法.
- 实验性评估超过100万批量生产的MTJ设备,以测量周期间的变化.
- 开发了一个离芯片训练方法来调整DNN参数.
主要成果:
- MTJ设备显示了超低的循环到循环变化.
- 拟议的离芯片培训实现了准确的AiMC推断.
- 验证了改进的传输曲线线性和减少MAC操作中的错误.
- 模拟的大规模神经网络模型与软件基线准确度相匹配.
结论:
- 基于MTJ的AiMC与离芯片校准实现高精度,没有芯片训练.
- 这种方法克服了memristor的耐力限制.
- MTJ 设备显示出有效和准确的 AI 加速任务的巨大潜力.
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