论文标题
使用受控的铁电域动力学有效的深度神经网络加速器
Efficient Deep Neural Network Accelerator Using Controlled Ferroelectric Domain Dynamics
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。
The current work reports an efficient deep neural network (DNN) accelerator where synaptic weight elements are controlled by ferroelectric domain dynamics. An integrated device-to-algorithm framework for benchmarking novel synaptic devices is used. In P(VDF-TrFE) based ferroelectric tunnel junctions, analog conductance states are measured using a custom pulsing protocol and associated custom circuits and array architectures for DNN training is simulated. Our results show precise control of polarization switching dynamics in multi-domain, polycrystalline ferroelectric thin films can produce considerable weight update linearity in metal-ferroelectric-semiconductor (MFS) tunnel junctions. Ultrafast switching and low junction current in these devices offer extremely energy efficient operation. Through an integrated platform of hardware development, characterization and modelling, we predict the available conductance range where linearity is expected under identical potentiating and depressing pulses for efficient DNN training and inference tasks. As an example, an analog crossbar based DNN accelerator with MFS junctions as synaptic weight elements showed ~ 93% training accuracy on large MNIST handwritten digit dataset while for cropped images, a 95% accuracy is achieved. One observed challenge is rather limited dynamic conductance range while operating under identical potentiating and depressing pulses below 1V. Investigation is underway for improving the dynamic conductance range without losing the weight update linearity.
