Training deep neural networks (DNNs) is computationally intensive but arrays of non-volatile memories like Charge Trap Flash (CTF) can accelerate DNN operations using in-memory computing. Specifically, the Resistive Processing Unit (RPU) architecture uses the voltage-threshold program by stochastic encoded pulse trains and analog memory features to accelerate vector-vector outer product and weight update for the gradient descent algorithms. Although CTF, offering high precision, has been regarded as an excellent choice for implementing RPU, the accumulation of charge due to the applied stochastic pulse trains is ultimately of critical significance in determining the final weight update. In this paper, we report the non-ideal program-time conservation in CTF through pulsing input measurements. We experimentally measure the effect of pulse width and pulse gap, keeping the total ON-time of the input pulse train constant, and report three non-idealities: (1) Cumulative V_T shift reduces when total ON-time is fragmented into a larger number of shorter pulses, (2) Cumulative V_T shift drops abruptly for pulse widths < 2 {\mu}s, (3) Cumulative V_T shift depends on the gap between consecutive pulses and the V_T shift reduction gets recovered for smaller gaps. We present an explanation based on a transient tunneling field enhancement due to blocking oxide trap-charge dynamics to explain these non-idealities. Identifying and modeling the responsible mechanisms and predicting their system-level effects during learning is critical. This non-ideal accumulation is expected to affect algorithms and architectures relying on devices for implementing mathematically equivalent functions for in-memory computing-based acceleration.

翻译：训练深度神经网络（DNN）计算密集，但电荷陷阱闪存（CTF）等非易失性存储器阵列可利用存内计算加速DNN运算。具体而言，电阻处理单元（RPU）架构采用随机编码脉冲序列的电压阈值编程方式与模拟存储特性，加速向量-向量外积计算及梯度下降算法中的权值更新。尽管具有高精度的CTF被视作实现RPU的优异选择，但施加随机脉冲序列所导致的电荷积累本质上对最终权值更新的确定具有关键意义。本文通过脉冲输入测量，报道了CTF中非理想的编程时间守恒现象。我们实验测量了保持输入脉冲序列总导通时间恒定条件下脉冲宽度与脉冲间隔的影响，并报道了三种非理想特性：（1）当总导通时间被分割为更多数量更短脉冲时，累积阈值电压漂移量降低；（2）当脉冲宽度小于2微秒时，累积阈值电压漂移量陡然下降；（3）累积阈值电压漂移量取决于相邻脉冲间隔，且该漂移量减小效应在较小间隔下得以恢复。我们提出基于阻挡氧化层陷阱电荷动态导致的瞬态隧穿场增强机制来解释这些非理想特性。识别并建模相关机理，预测其在学习过程中的系统级效应至关重要。这种非理想积累效应预计将影响那些依赖器件实现存内计算加速中数学等价函数的算法与架构。