Computing-in-Memory (CiM) architectures based on emerging non-volatile memory (NVM) devices have demonstrated great potential for deep neural network (DNN) acceleration thanks to their high energy efficiency. However, NVM devices suffer from various non-idealities, especially device-to-device variations due to fabrication defects and cycle-to-cycle variations due to the stochastic behavior of devices. As such, the DNN weights actually mapped to NVM devices could deviate significantly from the expected values, leading to large performance degradation. To address this issue, most existing works focus on maximizing average performance under device variations. This objective would work well for general-purpose scenarios. But for safety-critical applications, the worst-case performance must also be considered. Unfortunately, this has been rarely explored in the literature. In this work, we formulate the problem of determining the worst-case performance of CiM DNN accelerators under the impact of device variations. We further propose a method to effectively find the specific combination of device variation in the high-dimensional space that leads to the worst-case performance. We find that even with very small device variations, the accuracy of a DNN can drop drastically, causing concerns when deploying CiM accelerators in safety-critical applications. Finally, we show that surprisingly none of the existing methods used to enhance average DNN performance in CiM accelerators are very effective when extended to enhance the worst-case performance, and further research down the road is needed to address this problem.
翻译:以新兴的非挥发性内存(NVM)装置为基础的电子内存(CiM)结构显示,由于能效高,极有可能加速深神经网络(DNN)加速,但是,NVM装置由于各种非理想性,特别是由于装置的随机行为造成的装置到周期的变异而存在装置到装置的制造缺陷和周期的变异。因此,实际绘制到NVM装置的DNN重量可能大大偏离预期值,导致大规模性能退化。为解决这一问题,大多数现有工作的重点是在装置变异的情况下最大限度地提高平均性能。这个目标将有利于一般用途的假设。但对于安全临界应用来说,也必须考虑最坏的性能。不幸的是,文献中很少探讨这一点。在设备变异的情况下,我们提出了确定CIM DNNNC装置最坏性能的问题。我们进一步提出一个方法,在高空间的高度空间中找到设备变异性的具体组合,导致最坏的性能下降。我们最后发现,在甚小的轨道变性能中,我们使用的CNNPA的性能是如何提高。