NAND-SPIN-based processing-in-MRAM architecture for convolutional neural network acceleration

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 study, we propose a NAND-SPIN-based PIM architecture for efficient convolutional neural network (CNN) acceleration. A straightforward data mapping scheme is exploited to improve 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 similar to 2.6x speedup and similar to 1.4x improvement in energy efficiency over state-of-the-art PIM solutions.

Automated summary

To address the performance and efficiency issues of running large-scale datasets on traditional computing systems, a NAND-SPIN-based PIM architecture is proposed for efficient convolutional neural network (CNN) acceleration. A straightforward data mapping scheme is employed to improve parallelism and reduce data movements. Experimental results demonstrate that the approach achieves similar to 2.6x speedup and similar to 1.4x improvement in energy efficiency compared to state-of-the-art PIM solutions.