Power-Efficient Spiking Neural Networks

受人脑神经元和突触的启发，我们重点探讨了尖峰神经网络（SNNs）的优缺点。然后提出了可扩展和节能的snn，利用信号的时间（尖峰）来处理信息。脉冲本质上是一个二元事件，它要么是0，要么是1。snn的主要优点是可以充分利用时间和空间信息。snn使用时间作为额外的输入维度，以稀疏的方式记录有价值的信息。snn中的神经元只有在接收或产生峰值信号时才处于活动状态，这意味着它是由事件驱动的，因此可以节省能量。如果没有峰值到来，神经元将保持空闲状态。此外，SNN中的输入值为1或0，这也减少了乘法运算，从而减少了计算负荷。有几种方法可以对SNNS的输入数据进行编码。当研究降低神经网络硬件成本的方法时，随机计算（SC）因其简单的逻辑用于复杂的计算而变得具有吸引力。与传统的二进制编码不同，SC对随机生成的二进制序列进行操作。使用随机数对输入数据进行编码，snn被称为随机snn。在这个项目中，我们专注于随机snn的设计、评估和实现。我们还将探索市场机会，并努力将所开发的技术商业化。

Inspired by the neurons and synapses of human's brain, we focus on exploring the advantages and disadvantages of spiking neural networks (SNNs). Then scalable and energy-efficient SNNs have been proposed to use time (spikes) of the signal to process information. The spike is essentially a binary event, it is either 0 or 1. The main advantage of SNNs is that it can make the full use of time and space information. Using time as an additional input dimension, SNNs record valuable information in a sparse manner. The neuron in SNNs is in an active state only when receiving or generating a peak signal, which means that it is driven by events, so it can save energy. If there is no spike coming, the neuron will remain idle. In addition, the input value in SNN is 1 or 0, which also reduces the multiplication operation, to a less computation load. There are several ways to encode the input data of SNNS. When looking into ways to reduce the hardware cost of neural networks, stochastic computing (SC) becomes appealing due to the simple logic used for complex computation. Unlike the conventional binary encoding, SC operates on randomly generated binary sequences. Using random numbers to encode input data, the SNNs are called stochastic SNNs. In this project, we focus on the design, evaluation and implementation of stochastic SNNs. We will also explore the market opportunity and work toward the commercialization of the developed technology.