论文标题
MEMTORCH：MEMRISTIVE深度学习系统的开源模拟框架
MemTorch: An Open-source Simulation Framework for Memristive Deep Learning Systems
论文作者
论文摘要
回忆设备表现出了巨大的希望，可以促进加速度并提高深度学习（DL）系统的功率效率。使用这些电阻随机访问存储器（RRAM）设备构建的横杆体系结构可用于有效地实现各种内存计算操作，例如多重积累（MAC）和独立的相关性，这些操作在深层神经网络（DNNS）和卷积神经网络（CNNS）中大量使用。然而，回忆设备面临着衰老和非理想性的关注，这些设备限制了在电路级实现之前，应考虑备忘性深度学习系统（MDLS）的准确性，可靠性和鲁棒性。该原始软件出版物（OSP）介绍了Memtorch，这是一个开源框架，用于定制的大规模磁盘DL模拟，并精致着眼于对设备非理想性的共模拟。 Memtorch还促进了钥匙横梁外围电路的共同建模。 Memtorch采用现代化的软件工程方法，并直接与知名的Pytorch机器学习（ML）库集成
Memristive devices have shown great promise to facilitate the acceleration and improve the power efficiency of Deep Learning (DL) systems. Crossbar architectures constructed using these Resistive Random-Access Memory (RRAM) devices can be used to efficiently implement various in-memory computing operations, such as Multiply Accumulate (MAC) and unrolled-convolutions, which are used extensively in Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs). However, memristive devices face concerns of aging and non-idealities, which limit the accuracy, reliability, and robustness of Memristive Deep Learning Systems (MDLSs), that should be considered prior to circuit-level realization. This Original Software Publication (OSP) presents MemTorch, an open-source framework for customized large-scale memristive DL simulations, with a refined focus on the co-simulation of device non-idealities. MemTorch also facilitates co-modelling of key crossbar peripheral circuitry. MemTorch adopts a modernized soft-ware engineering methodology and integrates directly with the well-known PyTorch Machine Learning (ML) library
