用非易失性存储器加速机器学习:探索器件和电路的权衡

2016 IEEE International Conference on Rebooting Computing (ICRC) Pub Date : 2016-10-01 DOI:10.1109/ICRC.2016.7738684

Alessandro Fumarola, P. Narayanan, Lucas L. Sanches, Severin Sidler, Junwoo Jang, Kibong Moon, R. Shelby, H. Hwang, G. Burr

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引用次数: 29

摘要

为存储级存储器(SCM)开发的相同的非易失性存储器(NVM)的大阵列-例如相变存储器(PCM)和电阻RAM (ReRAM) -也可以用于非冯诺伊曼神经形态计算方案，器件电导作为突触“重量”。这允许在权重数据上有效地执行这些算法中最重要的乘法-累加操作。

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Accelerating machine learning with Non-Volatile Memory: Exploring device and circuit tradeoffs

Large arrays of the same nonvolatile memories (NVM) being developed for Storage-Class Memory (SCM) - such as Phase Change Memory (PCM) and Resistance RAM (ReRAM) - can also be used in non-Von Neumann neuromorphic computational schemes, with device conductance serving as synaptic “weight.” This allows the all-important multiply-accumulate operation within these algorithms to be performed efficiently at the weight data.

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2016 IEEE International Conference on Rebooting Computing (ICRC)

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