Computing-In-Memory Neural Network Accelerators for Safety-Critical Systems: Can Small Device Variations Be Disastrous?
Computing-In-Memory Neural Network Accelerators for Safety-Critical Systems: Can Small Device Variations Be Disastrous?
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DOI：
10.1145/3508352.3549360
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发表时间：
2022-07
期刊：
影响因子：
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通讯作者：
Zheyu Yan;X. Hu;Yiyu Shi
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文献类型：
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作者：
Zheyu Yan;X. Hu;Yiyu Shi
基于新出现的非挥发性记忆（NVM）设备的架构计算（CIM）的架构具有巨大的潜力，因为它们的高能量效率很高，但是NVM设备的高能量效果。映射到NVM设备可能会偏离预期的值，导致大多数的效果，大多数现有的作品集中在设备变化下的平均性能。在设备变化的影响下，CIM DNN加速器的性能有效地在高维空间中找到了设备变化的特定组合，从而导致案例的性能最差，即使是很小用于提高CIM加速器中的平均DNN性能的平均性能在扩展时非常有效，以提高最差的性能，并且需要进一步研究以解决此问题。
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.