论文标题
TNN7：一个定制宏套件，用于实施高度优化的神经形态TNNS设计
TNN7: A Custom Macro Suite for Implementing Highly Optimized Designs of Neuromorphic TNNs
论文作者
论文摘要
颞神经网络（TNNS），灵感来自哺乳动物新皮层，具有节能的在线感觉处理能力。最近的作品提出了一个微观结构框架，用于实施TNN，并在视觉和时间序列应用程序上表现出竞争性能。在这些工作的基础上，这项工作提出了TNN7，这是一套使用预测性7NM工艺设计套件（PDK）开发的九个高度优化的自定义宏，以提高TNN设计框架的效率，模块化和灵活性。用于两种应用的TNN原型用于评估TNN7。可以在40 UW功率和0.05 mm^2的区域内实施无监督的时间序列聚类TNN，而4层TNN仅达到1％的MNIST错误率，仅消耗18 MW和24.63 mm^2。平均而言，提议的宏平均将功率，延迟，面积和能量延迟产品降低了14％，16％，28％和45％。此外，采用TNN7大大降低了TNN设计的合成运行时（超过3倍），从而实现了高度缩放的TNN实现。
Temporal Neural Networks (TNNs), inspired from the mammalian neocortex, exhibit energy-efficient online sensory processing capabilities. Recent works have proposed a microarchitecture framework for implementing TNNs and demonstrated competitive performance on vision and time-series applications. Building on these previous works, this work proposes TNN7, a suite of nine highly optimized custom macros developed using a predictive 7nm Process Design Kit (PDK), to enhance the efficiency, modularity and flexibility of the TNN design framework. TNN prototypes for two applications are used for evaluation of TNN7. An unsupervised time-series clustering TNN delivering competitive performance can be implemented within 40 uW power and 0.05 mm^2 area, while a 4-layer TNN that achieves an MNIST error rate of 1% consumes only 18 mW and 24.63 mm^2. On average, the proposed macros reduce power, delay, area, and energy-delay product by 14%, 16%, 28%, and 45%, respectively. Furthermore, employing TNN7 significantly reduces the synthesis runtime of TNN designs (by more than 3x), allowing for highly-scaled TNN implementations to be realized.
