论文标题
关于深度神经网络计算机中计算机的可靠性
On the Reliability of Computing-in-Memory Accelerators for Deep Neural Networks
论文作者
论文摘要
具有新兴的非易失性记忆（NVCIM）的内存计算被证明是高能量效率加速深层神经网络（DNNS）的有前途的候选人。但是，大多数非易失性存储器（NVM）设备都遭受可靠性问题的困扰，从而导致NVCIM计算中涉及的实际数据与数据中心训练的重量值之间存在差异。因此，实际上部署在NVCIM平台上的模型比在常规硬件（例如GPU）上训练的对应者的精度较低。在本章中，我们首先简要介绍了NVCIM DNN加速器的机遇和挑战，然后展示不同类型的NVM设备的属性。然后，我们介绍了NVCIM DNN加速器的一般体系结构。之后，我们讨论了不可靠性的来源以及如何有效地建模其影响。最后，我们介绍了减轻设备变化影响的代表性作品。
Computing-in-memory with emerging non-volatile memory (nvCiM) is shown to be a promising candidate for accelerating deep neural networks (DNNs) with high energy efficiency. However, most non-volatile memory (NVM) devices suffer from reliability issues, resulting in a difference between actual data involved in the nvCiM computation and the weight value trained in the data center. Thus, models actually deployed on nvCiM platforms achieve lower accuracy than their counterparts trained on the conventional hardware (e.g., GPUs). In this chapter, we first offer a brief introduction to the opportunities and challenges of nvCiM DNN accelerators and then show the properties of different types of NVM devices. We then introduce the general architecture of nvCiM DNN accelerators. After that, we discuss the source of unreliability and how to efficiently model their impact. Finally, we introduce representative works that mitigate the impact of device variations.
