神经形态工程学
线性
功率(物理)
材料科学
计算机科学
计算机体系结构
电子工程
物理
人工神经网络
工程类
人工智能
量子力学
作者
Scott T. Keene,Armantas Melianas,Elliot James Fuller,Yoeri van de Burgt,A. Alec Talin,Alberto Salleo
标识
DOI:10.1088/1361-6463/aabe70
摘要
Neuromorphic devices are becoming increasingly appealing as efficient emulators of neural networks used to model real world problems. However, no hardware to date has demonstrated the necessary high accuracy and energy efficiency gain over CMOS in both (1) training via backpropagation and (2) in read via vector matrix multiplication. Such shortcomings are due to device non-idealities, particularly asymmetric conductance tuning in response to uniform voltage pulse inputs. Here, by formulating a general circuit model for capacitive ion-exchange neuromorphic devices, we show that asymmetric nonlinearity in organic electrochemical neuromorphic devices (ENODes) can be suppressed by an appropriately chosen write scheme. Simulations based upon our model suggest that a nonlinear write-selector could reduce the switching voltage and energy, enabling analog tuning via a continuous set of resistance states (100 states) with extremely low switching energy (~170 fJ · µm−2). This work clarifies the pathway to neural algorithm accelerators capable of parallelism during both read and write operations.
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