广义键值记忆在记忆增强网络中的冗余调整
Generalized Key-Value Memory to Flexibly Adjust Redundancy in Memory-Augmented Networks
摘要：
记忆增强的神经网络通过外部键值记忆来增强神经网络，其复杂性通常由键记忆中的支持向量的数量决定。我们提出了一个广义的键值存储器，通过引入一个自由参数，可以任意添加或删除冗余的键存储器表示，从支持向量的数量，它的维数。实际上，它提供了额外的自由度来灵活地控制鲁棒性与存储和计算广义键值存储器所需的资源之间的权衡。这对于在存内计算硬件上实现密钥存储器特别有用，其中密钥存储器利用非理想但极其有效的非易失性存储器设备进行密集存储和计算。实验结果表明，适应这个参数的需求，有效地减轻高达44%的非理想性，在相同的精度和设备数量，而不需要任何神经网络再训练。
英文摘要：
Memory-augmented neural networks enhance a neural network with an external key-value memory whose complexity is typically dominated by the number of support vectors in the key memory. We propose a generalized key-value memory that decouples its dimension from the number of support vectors by introducing a free parameter that can arbitrarily add or remove redundancy to the key memory representation. In effect, it provides an additional degree of freedom to flexibly control the trade-off between robustness and the resources required to store and compute the generalized key-value memory. This is particularly useful for realizing the key memory on in-memory computing hardware where it exploits nonideal, but extremely efficient non-volatile memory devices for dense storage and computation. Experimental results show that adapting this parameter on demand effectively mitigates up to 44% nonidealities, at equal accuracy and number of devices, without any need for neural network retraining.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
