论文标题
基于NAND-SPIN的卷积神经网络加速的MRAM架构
NAND-SPIN-Based Processing-in-MRAM Architecture for Convolutional Neural Network Acceleration
论文作者
论文摘要
由于现有的“电壁”和“记忆墙”问题，在传统计算系统上运行大规模数据集的性能和效率表现出关键的瓶颈。为了解决这些问题，开发了内存处理（PIM）架构以在记忆中或附近的记忆中带来计算逻辑，以减轻数据传输过程中的带宽限制。 NAND状的Spintronics Memory（NAND-Spin）是一种具有低写入能量和高积分密度的有前途的磁性随机访问记忆（MRAM），并且可以使用它来执行有效的内存计算操作。在这项工作中，我们提出了一种基于NAND的PIM架构，用于有效的卷积神经网络（CNN）加速。利用直接的数据映射方案来改善并行性，同时减少数据移动。从NAND自旋和内存处理架构的出色特征中受益，实验结果表明，所提出的方法可以实现$ \ sim $ \ sim $ 2.6 $ \ times $ speedup和$ \ sim $ \ sim $ 1.4 $ \ times $ $ \ times $提高能源效率比先进的PIM解决方案。
The performance and efficiency of running large-scale datasets on traditional computing systems exhibit critical bottlenecks due to the existing "power wall" and "memory wall" problems. To resolve those problems, processing-in-memory (PIM) architectures are developed to bring computation logic in or near memory to alleviate the bandwidth limitations during data transmission. NAND-like spintronics memory (NAND-SPIN) is one kind of promising magnetoresistive random-access memory (MRAM) with low write energy and high integration density, and it can be employed to perform efficient in-memory computation operations. In this work, we propose a NAND-SPIN-based PIM architecture for efficient convolutional neural network (CNN) acceleration. A straightforward data mapping scheme is exploited to improve the parallelism while reducing data movements. Benefiting from the excellent characteristics of NAND-SPIN and in-memory processing architecture, experimental results show that the proposed approach can achieve $\sim$2.6$\times$ speedup and $\sim$1.4$\times$ improvement in energy efficiency over state-of-the-art PIM solutions.
