Farzana Ahmed Siddique

The disparity between processor speed and memory bandwidth has become a growing performance bottleneck, particularly for memory-intensive workloads. Processing-in-Memory (PIM) mitigates this bottleneck by integrating computation directly within DRAM. However, the effectiveness of PIM varies significantly across workloads, architectures, and DRAM technologies, yet it is often assessed using tightly coupled simulators and benchmarks that lack portability and generality. This article extends PIMbench and PIMeval —a generalizable benchmark suite and an extensible PIM simulator to support a broader range of workloads, PIM architectures, and DRAM technologies. This evaluation incorporates roofline analysis and a breakdown of intra-memory execution stages to identify PIM-specific bottlenecks and performance scaling limits. The evaluation spans three classes of digital PIM architectures: subarray-level bit-serial, subarray-level bit-parallel, and bank-level bit-parallel. It further demonstrates how internal DRAM parameters such as subarray count and GDL width impact PIM performance. The code is publicly available at: https://github.com/UVA-LavaLab/PIMeval-PIMbench .