In-Memory Computing in Emerging Memory Technologies for Machine Learning: An Overview
In-Memory Computing in Emerging Memory Technologies for Machine Learning: An Overview
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用于机器学习的新兴内存技术中的内存计算：概述
DOI：
10.1109/dac18072.2020.9218505
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Amogh Agrawal
中科院分区：
文献类型：
--
作者：
K. Roy;I. Chakraborty;M. Ali;Aayush Ankit;Amogh Agrawal
CMOS技术的饱和扩展趋势推动了对新兴非易失性存储器（NVM）技术的探索，将其作为加速数据密集型机器学习（ML）工作负载的有前途的替代方案。为此，研究人员已经探索了基于NVM交叉开关基元的专用加速器。NVM crossbar具有高存储密度，可以有效地执行大规模并行原位矩阵向量乘法（MVM）操作，这是ML工作负载中的关键计算，有助于克服冯诺依曼架构面临的内存瓶颈。尽管有这些承诺，但NVM交叉开关的模拟计算性质可能由于器件和电路的非理想性（诸如寄生电阻和器件非线性）而导致功能错误。此外，NVM交叉开关需要高成本的外围电路来集成在大规模系统中。因此，有必要研究不同层次的设计堆栈，以实现这种technology.In本文中，我们提出了一个概述的内存计算在NVM的交叉机器学习工作负载。我们讨论了NVM交叉开关的基本解剖，并强调在原始水平所面临的挑战。接下来，我们介绍了NVM交叉开关的高存储密度如何能够实现空间分布式架构。此外，我们提出了各种建模和评估工具，可以有效地帮助我们研究的功能，以及NVM交叉系统的性能。最后，对该领域未来的研究方向进行了展望。
The saturating scaling trends of CMOS technology have fuelled the exploration of emerging non-volatile memory (NVM) technologies as a promising alternative for accelerating data intensive Machine Learning (ML) workloads. To that effect, researchers have explored special-purpose accelerators based on NVM crossbar primitives. NVM crossbars have high storage density and can efficiently per-form massively parallel in-situ Matrix Vector Multiplication (MVM) operations, the key computation in ML workloads, helping over-come the memory bottleneck faced by von Neumann architectures. Despite the promises, analog computing nature of NVM crossbars can lead to functional errors due to device and circuit non-idealities such as parasitic resistances and device non-linearities. Moreover, NVM crossbars need high cost peripheral circuitry to be integrated in large scale systems. Hence, there is a need to study different levels of the design stack to realize the potential of this technology.In this paper, we present an overview of in-memory computing in NVM crossbars for ML workloads. We discuss the basic anatomy of NVM crossbars and highlight the challenges faced at the primitive level. Next, we present how the high storage density of NVM crossbars can enable spatially distributed architectures. Further, we present various modeling and evaluation tools which can effectively help us study the functionality as well as performance of NVM crossbar systems. Finally, we provide an outlook on the future research directions in this field.
DOI：
10.1109/tcad.2018.2789723
发表时间：
2018-12-01
影响因子：
2.9
作者：
Chen, Pai-Yu;Peng, Xiaochen;Yu, Shimeng
通讯作者：
Yu, Shimeng