Naous Rawan, Siemon Anne, Schulten Michael, Alahmadi Hamzah, Kindsmüller Andreas, Lübben Michael, Heittmann Arne, Waser Rainer, Salama Khaled Nabil, Menzel Stephan
Department of Electrical Engineering and Computer Science, University of California Berkeley, Berkeley, CA, USA.
Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.
Sci Rep. 2021 Feb 18;11(1):4218. doi: 10.1038/s41598-021-83382-y.
The inevitable variability within electronic devices causes strict constraints on operation, reliability and scalability of the circuit design. However, when a compromise arises among the different performance metrics, area, time and energy, variability then loosens the tight requirements and allows for further savings in an alternative design scope. To that end, unconventional computing approaches are revived in the form of approximate computing, particularly tuned for resource-constrained mobile computing. In this paper, a proof-of-concept of the approximate computing paradigm using memristors is demonstrated. Stochastic memristors are used as the main building block of probabilistic logic gates. As will be shown in this paper, the stochasticity of memristors' switching characteristics is tightly bound to the supply voltage and hence to power consumption. By scaling of the supply voltage to appropriate levels stochasticity gets increased. In order to guide the design process of approximate circuits based on memristors a realistic device model needs to be elaborated with explicit emphasis of the probabilistic switching behavior. Theoretical formulation, probabilistic analysis, and simulation of the underlying logic circuits and operations are introduced. Moreover, the expected output behavior is verified with the experimental measurements of valence change memory cells. Hence, it is shown how the precision of the output is varied for the sake of the attainable gains at different levels of available design metrics. This approach represents the first proposition along with physical verification and mapping to real devices that combines stochastic memristors into unconventional computing approaches.
电子设备中不可避免的变异性对电路设计的操作、可靠性和可扩展性造成了严格限制。然而，当不同性能指标（面积、时间和能量）之间出现折衷时，变异性会放宽严格的要求，并允许在替代设计范围内进一步节省资源。为此，非传统计算方法以近似计算的形式得以复兴，尤其适用于资源受限的移动计算。本文展示了使用忆阻器的近似计算范式的概念验证。随机忆阻器被用作概率逻辑门的主要构建模块。如本文将展示的，忆阻器开关特性的随机性与电源电压紧密相关，进而与功耗相关。通过将电源电压缩放至适当水平，随机性会增加。为了指导基于忆阻器的近似电路设计过程，需要精心构建一个现实的器件模型，并明确强调概率开关行为。本文介绍了基础逻辑电路和操作的理论公式、概率分析及仿真。此外，通过价态变化存储单元的实验测量验证了预期的输出行为。因此，展示了如何为了在不同可用设计指标水平上获得可实现的增益而改变输出精度。这种方法是将随机忆阻器融入非传统计算方法的首个提议，并伴有物理验证及与实际器件的映射。
