论文标题
快速探索降低精度对峰值神经网络的影响
Fast Exploration of the Impact of Precision Reduction on Spiking Neural Networks
论文作者
论文摘要
近似计算技术（AXC）技术以绩效，能源和区域降低增长的计算准确性。当应用程序本质上容忍某些准确性损失（如尖峰神经网络（SNNS）案例）时，权衡特别方便。当目标硬件达到计算的边缘时，SNN是一个实用的选择，但这需要某些领域最小化的策略。在这项工作中，我们采用间隔算术（IA）模型来开发一种探索方法，该方法利用了这种模型的能力传播近似误差以检测近似值何时超过应用程序可容忍的限制。实验结果证实了大大减少探索时间的能力，从而有机会进一步降低网络参数的大小并获得更细粒度的结果。
Approximate Computing (AxC) techniques trade off the computation accuracy for performance, energy, and area reduction gains. The trade-off is particularly convenient when the applications are intrinsically tolerant to some accuracy loss, as in the Spiking Neural Networks (SNNs) case. SNNs are a practical choice when the target hardware reaches the edge of computing, but this requires some area minimization strategies. In this work, we employ an Interval Arithmetic (IA) model to develop an exploration methodology that takes advantage of the capability of such a model to propagate the approximation error to detect when the approximation exceeds tolerable limits by the application. Experimental results confirm the capability of reducing the exploration time significantly, providing the chance to reduce the network parameters' size further and with more fine-grained results.
