A Survey on the Expanding Scope and Interdisciplinary Opportunities for Processing-in-Memory Techniques

Journal Article · Mon Feb 02 00:00:00 EST 2026 · IEEE Access

DOI:https://doi.org/10.1109/ACCESS.2026.3659051· OSTI ID:3019921

Asifuzzaman, Kazi ^[1]; He, Yuan ^[2]; Zhang, Tianyun ^[3]; Tang, Eric ^[3]; Rao Miniskar, Narasinga ^[1]; Teranishi, Keita ^[1]; Vetter, Jeffrey S. ^[1]

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1. Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
2. RIKEN Center for Computational Science, Tokyo (Japan)
3. Carnegie Mellon Univ., Pittsburgh, PA (United States)

Processing-in-Memory (PIM) is emerging as a practical path to overcome the limitations of traditional von Neumann architectures. At its core, PIM systems implement computing primitives such as logic operations and multiply-accumulate acceleration through compute-in-memory, near-memory processing, or hybrid designs. The role of memory cells varies widely across technologies, acting as inputs, outputs, or analog accumulators through bit-lines and sense amplifiers. This diversity creates trade-offs in precision, bandwidth, latency, and programmability, making it difficult to build a unified understanding on the progress of the field. In this survey, we organize recent advances of PIM into three areas. First, we discuss the progress on the architectural optimizations of PIM and its integration with both DRAM and emerging non-volatile memories. Second, we examine how PIM is being used to accelerate key computing domains, including generative AI workloads and high-performance kernels, along with new approaches. Third, we highlight the growing adoption of PIM in computational sciences, where it is being applied to solve interdisciplinary problems such as genome analysis, mRNA quantification, mass spectrometry, quantum circuit simulation, wave modeling, and secure computation. Finally, we synthesize the major challenges that continue to slow PIM adoption, including manufacturing constraints, power delivery, thermal reliability, data consistency, runtime and memory-management coordination, and the difficulty of building portable software abstractions without sacrificing commercial viability. This work provides an updated, structured perspective on PIM’s potential across computing and computational sciences and the barriers that must be solved for it to reach its full impact.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
• Grant/Contract Number: AC05-00OR22725; SC0025645
• Other Award/Contract Number: FOA-0003264; FWPERKJ452
• OSTI ID: 3019921
• Journal Information: IEEE Access, Journal Name: IEEE Access Vol. 14; ISSN 2169-3536
• Publisher: IEEE
• Country of Publication: United States
• Language: English