Non-Volatile Memory (NVM) cells are used in neuromorphic hardware to store model parameters, which are programmed as resistance states. NVMs suffer from the read disturb issue, where the programmed resistance state drifts upon repeated access of a cell during inference. Resistance drifts can lower the inference accuracy. To address this, it is necessary to periodically reprogram model parameters (a high overhead operation). We study read disturb failures of an NVM cell. Our analysis show both a strong dependency on model characteristics such as synaptic activation and criticality, and on the voltage used to read resistance states during inference. We propose a system software framework to incorporate such dependencies in programming model parameters on NVM cells of a neuromorphic hardware. Our framework consists of a convex optimization formulation which aims to implement synaptic weights that have more activations and are critical, i.e., those that have high impact on accuracy on NVM cells that are exposed to lower voltages during inference. In this way, we increase the time interval between two consecutive reprogramming of model parameters. We evaluate our system software with many emerging inference models on a neuromorphic hardware simulator and show a significant reduction in the system overhead.
翻译:非挥发性内存(NVM)细胞用于神经形态硬件中存储模型参数,这些参数是作为抗力状态编程的。 NVM 存在阅读扰动问题,即被编程的抗力状态在推论期间反复进入细胞时会漂浮。抵抗性漂移可以降低推断准确性。为此,有必要定期重编模型参数(高俯冲操作)。我们研究的是NVM 细胞的扰动故障。我们的分析表明,它非常依赖模型特性,如神经形态硬件的合成激活和临界性,以及用于在推断期间阅读抗力状态的电压。我们提议了一个系统软件框架,将这种依赖性特性纳入突变性细胞的编程模型参数中。我们的框架包括一个convex优化配制,目的是执行具有更多激活和关键作用的合成重量,即那些对NVM细胞的精度有高度影响,这些细胞在推导过程中会暴露在较低挥发性状态下。我们用这个方法,我们增加了两个连续的神经形态变变软体之间的时间间隔。我们用许多新的神经形态变变软体变压系统来显示。