论文标题
可穿戴巨大:基于织物的尖峰神经网络
Wearable uBrain: Fabric Based-Spiking Neural Network
论文作者
论文摘要
储层计算是预测湍流的有力工具,其简单的架构具有处理大型系统的计算效率。然而,其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间,然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用,该系统模拟了湍流的若干特征。我们表明,使用径向基函数作为非线性投影器,即使只有部分观测并且不知道控制方程,也能稳健地捕捉复杂的系统非线性。最后,我们表明,当测量稀疏、不完整且带有噪声,甚至控制方程变得不准确时,我们的网络仍然可以产生相当准确的预测,从而为实际湍流系统的无模型预测铺平了道路。
On garment intelligence influenced by artificial neural networks and neuromorphic computing is emerging as a research direction in the e-textile sector. In particular, bio inspired Spiking Neural Networks mimicking the workings of the brain show promise in recent ICT research applications. Taking such technological advancements and new research directions driving forward the next generation of e-textiles and smart materials, we present a wearable micro Brain capable of event driven artificial spiking neural network computation in a fabric based environment. We demonstrate a wearable Brain SNN prototype with multi-layer computation, enabling scalability and flexibility in terms of modifications for hidden layers to be augmented to the network. The wearable micro Brain provides a low size, weight and power artificial on-garment intelligent wearable solution with embedded functionality enabling offline adaptive learning through the provision of interchangeable resistor synaptic weightings. The prototype has been evaluated for fault tolerance, where we have determine the robustness of the circuit when certain parts are damaged. Validations were also conducted for movements to determine if the circuit can still perform accurate computation.