首页> 外文期刊>IEEE Journal on Exploratory Solid-State Computational Devices and Circuits >Non-Boolean Computing Benchmarking for Beyond-CMOS Devices Based on Cellular Neural Network
【24h】

Non-Boolean Computing Benchmarking for Beyond-CMOS Devices Based on Cellular Neural Network

机译:基于细胞神经网络的超越CMOS器件的非布尔计算基准测试

获取原文
获取原文并翻译 | 示例

摘要

This paper presents a uniform benchmarking methodology for non-Boolean computation based on the cellular neural network (CNN) for a variety of beyond-CMOS device technologies, including charge-based and spintronic devices. Three types of CNN implementations are investigated using analog, digital, and spintronic circuits. Monte Carlo simulations are performed to quantify the impact of the input noise, thermal noise, and the number of bits representing the weights of synapses on the overall recall probability and delay. The results demonstrate that the recall probability improves significantly as the number of synapses increase. Using a 4-b resolution for synapse weights provides the best tradeoff between the required numbers of synapses and synapse bits for a target recall rate. Finally, three types of CNN implementations with various device technologies are benchmarked for a given input noise and recall accuracy target. It is shown that spintronic devices are promising candidates to implement CNNs, where up to 3× energy-delay product improvement is predicted in domain wall devices compared to its conventional CMOS counterpart.
机译:本文介绍了一种基于蜂窝神经网络(CNN)的非布尔计算的统一基准测试方法,该方法适用于多种超越CMOS的器件技术,包括基于电荷的器件和自旋电子器件。使用模拟,数字和自旋电子电路研究了三种CNN实现方式。进行蒙特卡洛模拟以量化输入噪声,热噪声以及代表突触权重的位数对总体召回概率和延迟的影响。结果表明,召回概率随着突触数量的增加而显着提高。对突触权重使用4-b分辨率可在所需的突触数量和目标召回率的突触位之间提供最佳折衷。最后,针对给定的输入噪声和召回精度目标,对采用各种设备技术的三种CNN实现进行了基准测试。结果表明,自旋电子器件是实现CNN的有希望的候选者,与传统的CMOS同类器件相比,领域壁器件预计可将CNN的能量延迟产品提高3倍。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号