一个由大脑启发的计算系统等级
Youhui Zhang1,2,3, Peng Qu4,5,6, Yu Ji4,5,6
1Department of Computer Science and Technology, Tsinghua University, Beijing, China. zyh02@tsinghua.edu.cn.
Nature
|October 15, 2020
概括
神经形态计算缺乏一个通用的系统层次结构. 这项研究引入了"神经形态完整性"和系统层次,以确保编程语言可移植性和硬件兼容性,以实现大脑启发的人工智能开发.
科学领域:
- 计算机工程
- 人工智能
- 神经科学
背景情况:
- 由大脑启发的神经形态计算为下一代计算机工程和人工智能提供了潜力.
- 目前的脑启发式计算缺乏通用的系统层次和完整性的理解,阻碍了软件-硬件兼容性和开发生产力.
研究的目的:
- 提出"神经形态完整性"和相应的系统层次结构,用于大脑启发的计算.
- 解决当前神经形态系统中缺乏编程语言可移植性和硬件兼容性的问题.
主要方法:
- 引入了"神经形态完整性",放松了硬件完整性的要求.
- 开发了一个具有图灵完整软件抽象模型和多功能抽象神经形态架构的系统层次结构.
- 在各种神经形态硬件平台上执行程序的工具链软件.
主要成果:
- 建立了一个系统层次结构,使神经形态完整硬件实现统一的程序表示和转换.
- 已证明编程语言可移植性,硬件完整性和编译可行性.
- 通过神经形态的完整性引入了一个新的系统设计维度.
结论:
- 拟议的系统层次和神经形态完整性促进了大脑启发的计算的高效和兼容性进步.
- 这一框架有望加速包括通用人工智能在内的各种应用的发展.
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