论文标题
峰值神经网络能效的分析估计
An Analytical Estimation of Spiking Neural Networks Energy Efficiency
论文作者
论文摘要
尖刺神经网络是一种神经网络，仅使用尖峰进行神经元进行通信。它们通常被视为古典神经网络的低功率替代品，但是很少有作品证明这些主张是正确的。在这项工作中，我们提出了一个指标，可以独立于特定硬件估算SNN的能源消耗。然后，我们将此指标应用于SNNS处理现实世界应用程序的三种不同数据类型（静态，动态和事件）的代表。结果，我们所有的SNN的效率是FNN的效率6到8倍。
Spiking Neural Networks are a type of neural networks where neurons communicate using only spikes. They are often presented as a low-power alternative to classical neural networks, but few works have proven these claims to be true. In this work, we present a metric to estimate the energy consumption of SNNs independently of a specific hardware. We then apply this metric on SNNs processing three different data types (static, dynamic and event-based) representative of real-world applications. As a result, all of our SNNs are 6 to 8 times more efficient than their FNN counterparts.
