  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.
