Learning of deep neural networks (DNN) using Resistive Processing Unit (RPU)
architecture is energy-efficient as it utilizes dedicated neuromorphic hardware
and stochastic computation of weight updates for in-memory computing. Charge
Trap Flash (CTF) devices can implement RPU-based weight updates in DNNs.
However, prior work has shown that the weight updates (V_T) in CTF-based RPU
are impacted by the non-ideal program time of CTF. The non-ideal program time
is affected by two factors of CTF. Firstly, the effects of the number of input
pulses (N) or pulse width (pw), and secondly, the gap between successive update
pulses (t_gap) used for the stochastic computation of weight updates.
Therefore, the impact of this non-ideal program time must be studied for neural
network training simulations. In this study, Firstly, we propose a pulse-train
design compensation technique to reduce the total error caused by non-ideal
program time of CTF and stochastic variance of a network. Secondly, we simulate
RPU-based DNN with non-ideal program time of CTF on MNIST and Fashion-MNIST
datasets. We find that for larger N (~1000), learning performance approaches
the ideal (software-level) training level and, therefore, is not much impacted
by the choice of t_gap used to implement RPU-based weight updates. However, for
lower N (<500), learning performance depends on T_gap of the pulses. Finally,
we also performed an ablation study to isolate the causal factor of the
improved learning performance. We conclude that the lower noise level in the
weight updates is the most likely significant factor to improve the learning
performance of DNN. Thus, our study attempts to compensate for the error caused
by non-ideal program time and standardize the pulse length (N) and pulse gap
(t_gap) specifications for CTF-based RPUs for accurate system-level on-chip
training.

通过使用阻性处理单元（RPU）架构学习深度神经网络（DNN）可以实现高效能的能源利用，因为它利用专用的神经形态硬件和在内存计算中进行随机加权更新。我们提出了一种脉冲列设计补偿技术来降低由非理想程序时间和网络随机方差引起的总误差，并在 MNIST 和 Fashion-MNIST 数据集上模拟了基于非理想程序时间的 CTF，发现较大的脉冲数（~1000）时，学习性能接近理想（软件级）训练水平，而较小的脉冲数（<500）时，学习性能取决于脉冲间隔（t_gap）。此外，通过消融研究，我们确定了改进学习性能的关键因素是权重更新的噪声水平较低。因此，我们的研究旨在补偿由非理想程序时间引起的误差，并为 CTF 基础的 RPU 标准化脉冲长度（N）和脉冲间隔（t_gap）规格以实现准确的芯片内系统级训练。