Link between energy and computation in a physical model of Hopfield network
Link between energy and computation in a physical model of Hopfield network
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Hopfield 网络物理模型中能量与计算之间的联系
DOI：
10.1109/iconip.2002.1202175
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发表时间：
2002
期刊：
影响因子：
--
通讯作者：
Chakravarthy
中科院分区：
文献类型：
--
作者：
Abhishek Kumar;Manmohan;Uday Shankar;M. Vishwanathan;Chakravarthy
Landauer（1961）通过热力学将信息处理和能量流联系起来，提出不可逆的计算过程具有不可避免的“热力学成本”。我们探索在联想记忆的神经网络模型中这种联系的存在。我们对 Hopfield 神经网络的电子实现进行的模拟表明，只有通过增加热量形式的能量耗散才能获得网络性能的增强。相反，尽量减少能量耗散的努力会导致性能受损。
Linking information processing and energy flows via thermodynamics, Landauer (1961) proposed that irreversible computational processes have an inevitable "thermodynamic cost". We explore the existence of such a link in case of a neural network model of associative memory. Our simulations with an electronic implementation of the Hopfield neural network showed that enhanced performance of the network could only be obtained by increased dissipation of energy as heat. Contrarily, efforts to minimize energy dissipation led to impaired performance.