Processing-in-memory (PIM) architectures are emerging to reduce data movement in data-intensive applications. These architectures seek to exploit the same physical devices for both information storage and logic, thereby dwarfing the required data transfer and utilizing the full internal memory bandwidth. Whereas analog PIM utilizes the inherent connectivity of crossbar arrays for approximate matrix-vector multiplication in the analog domain, digital PIM architectures enable bitwise logic operations with massive parallelism across columns of data within memory arrays. Several recent works have extended the computational capabilities of digital PIM architectures towards the full-precision (single-precision floating-point) acceleration of convolutional neural networks (CNNs); yet, they lack a comprehensive comparison to GPUs. In this paper, we examine the potential of digital PIM for CNN acceleration through an updated quantitative comparison with GPUs, supplemented with an analysis of the overall limitations of digital PIM. We begin by investigating the different PIM architectures from a theoretical perspective to understand the underlying performance limitations and improvements compared to state-of-the-art hardware. We then uncover the tradeoffs between the different strategies through a series of benchmarks ranging from memory-bound vectored arithmetic to CNN acceleration. We conclude with insights into the general performance of digital PIM architectures for different data-intensive applications.

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