在神经形态计算机上使用虚拟神经元编码整数和理数
Prasanna Date1, Shruti Kulkarni2, Aaron Young2
1Oak Ridge National Laboratory, Oak Ridge, TN, 37830, USA. datepa@ornl.gov.
Scientific reports
|July 6, 2023
概括
神经形态计算机可以使用新的虚拟神经元抽象来执行通用计算,以实现高效的数字编码. 这一突破为未来的计算任务带来了显著的能源节约.
科学领域:
- 神经科学是一个神经科学.
- 计算机科学 计算机科学
- 节能计算 节能计算 节能计算 节能计算
背景情况:
- 神经形态计算机模仿人类大脑进行高效的计算,主要是在神经网络中.
- 尽管理论上的图灵完整性,但高效的数据编码仍然是神经形态硬件上通用计算的主要挑战.
- 现有的编码方法,如区分和基于速率的编码,对于广泛的计算应用是不够的.
研究的目的:
- 引入一种新的虚拟神经元抽象,用于高效编码和整数和理数的加法.
- 通过克服当前数据编码的局限性,在神经形态平台上实现通用计算.
- 评估这个新的编码机制的性能和能源效率.
主要方法:
- 开发了一个虚拟神经元抽象,使用尖端神经网络原始.
- 实现和评估虚拟神经元的编码和添加数字.
- 在物理和模拟的神经形态硬件上测试了性能,包括基于memristor的处理器.
- 在μ递归函数中证明了实用性,这是通用计算的基础.
主要成果:
- 虚拟神经元抽象能够有效编码和整数和理数的加法.
- 据估计,使用基于memristor的神经形态处理器的加法操作平均只需要23nJ.
- 该方法成功地集成到μ递归函数中,展示了其用于通用计算的潜力.
结论:
- 虚拟神经元抽象是一种可行的解决方案,可以在神经形态计算机上有效编码数字.
- 这种方法显著提高了神经形态系统在节能通用计算方面的潜力.
- 进一步的研究可以利用这种机制来释放由大脑启发的计算架构的全部功能.
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