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用闭环映射策略接近映射极限,用于在神经形态硬件上部署神经网络

Song Wang¹, Qiushuang Yu¹, Tiantian Xie¹

¹Department of Precision Instrument, Center for Brain-Inspired Computing Research (CBICR), Tsinghua University, Beijing, China.

Frontiers in neuroscience

|June 5, 2023

概括

本研究介绍了神经形态芯片的映射极限概念,优化神经网络部署. 一种新的闭环策略可以在分散的多核心架构上提高资源利用率和处理效率.

科学领域:

计算机工程计算机工程
人工智能的人工智能
硬件架构硬件架构

背景情况:

由于并行性和内存局部性,分散的多核心架构在神经形态芯片中很常见.
在这些架构上部署神经网络模型在资源利用和处理效率方面面临挑战,原因是记忆碎片化和分散执行.

研究的目的:

在神经网络在神经形态硬件上的逻辑和物理映射中引入"映射极限"的概念,以节省资源.
提出一种新的闭环测绘策略,以提高部署效率.

主要方法:

引入了"映射极限"概念,以定义节约资源的上限.
开发了一个闭环映射策略,涉及异步4D模型分区用于逻辑映射.
使用汉密尔顿循环算法 (HLA) 进行物理映射.

主要成果:

在TianjicX神经形芯片上实施了拟议的映射方法.
与现有方法相比,表现出优越的性能.
取得的结果接近定义的映射极限.

结论:

拟议的映射极限概念和闭环策略在神经形态硬件上的神经网络部署中提供了显著的改进.
这些进展可以为神经形态系统的一般和高效的映射框架做出贡献.

关键词:

封闭循环映射的映射方法这是逻辑映射的逻辑映射.绘制地图的极限值.这是一个神经形态芯片.物理地图绘制物理地图绘制

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