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对神经形态硬件使用模拟非易失性记忆的高效混合训练方法.

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    一种新的混合训练方法通过避免昂贵的导电性调节来提高神经形态硬件的准确性. 这种方法大大降低了人工智能应用的培训成本和能源消耗.

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    科学领域:

    • 神经形态工程的神经形态工程
    • 人工智能硬件是人工智能的硬件.

    背景情况:

    • 带有模拟突触装置的神经形态硬件为矢量矩阵乘法 (VMM) 提供了能源和时间效率.
    • 对于这种硬件的现有培训方法由于模拟设备的非理想性和与导电性调节协议相关的高培训成本而导致精度降低.

    研究的目的:

    • 为神经形态硬件提出并实验证明一种新的混合训练方法,可以克服当前训练方法的局限性.
    • 为了降低训练非挥发性模拟突触器件的成本和复杂性,同时保持高精度.

    主要方法:

    • 开发一种混合训练方法,可以绕过对导电性调节协议的需求,以更新模拟突触器件中的权重.
    • 使用制造的神经形态硬件对拟议的训练方法进行实验验证.

    主要成果:

    • 混合培训方法显著降低了在线培训成本.
    • 基于硬件的神经网络的准确性在仅经过一个训练时代后就接近基于软件的准确性,即使仅训练了第一个突触层.
    • 该方法在各种突触器件中表现出高度非线性重量更新特征的有效性.

    结论:

    • 拟议的混合训练方法为训练神经形态硬件提供了一种高效且具有成本效益的解决方案.
    • 这种方法提高了使用非挥发性模拟记忆细胞的神经形态硬件的可行性,作为未来人工智能的有希望平台.
    • 该方法适用于低功耗的神经形态系统和各种突触器件的适用性突出了其广泛的潜力.