The DOI matches its Crossref record.
TOP-PIM: throughput-oriented programmable processing in memory
Abstract
As computation becomes increasingly limited by data movement and energy consumption, exploiting locality throughout the memory hierarchy becomes critical to continued performance scaling. Moving computation closer to memory presents an opportunity to reduce both energy and data movement overheads. We explore the use of 3D die stacking to move memory-intensive computations closer to memory. This approach to processing in memory addresses some drawbacks of prior research on in-memory computing and is commercially viable in the foreseeable future.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
PipeLayer: A Pipelined ReRAM-Based Accelerator for Deep Learning
Song, L., Qian, X., Li, H., & Chen, Y. (2017) · 2017 IEEE International Symposium on High Performance Computer Architecture (HPCA) · doi:10.1109/hpca.2017.55 · Cited by 833
Computing in Memory With Spin-Transfer Torque Magnetic RAM
Jain, S., Ranjan, A., Roy, K., & Raghunathan, A. (2018) · IEEE Transactions on Very Large Scale Integration (VLSI) Systems · doi:10.1109/tvlsi.2017.2776954 · Cited by 355
Practical Near-Data Processing for In-Memory Analytics Frameworks
Gao, M., Ayers, G., & Kozyrakis, C. (2015) · 2015 International Conference on Parallel Architecture and Compilation (PACT) · doi:10.1109/pact.2015.22 · Cited by 266
In-Memory Data Parallel Processor
Fujiki, D., Mahlke, S., & Das, R. (2018) · Proceedings of the Twenty-Third International Conference on Architectural Support for Programming Languages and Operating Systems · doi:10.1145/3173162.3173171 · Cited by 109
Data-Centric Computing Frontiers: A Survey On Processing-In-Memory
Siegl, P., Buchty, R., & Berekovic, M. (2016) · Proceedings of the Second International Symposium on Memory Systems · doi:10.1145/2989081.2989087 · Cited by 55
Metadata
- Authors
- Zhang, D., Jayasena, N., Lyashevsky, A., Greathouse, J. L., Xu, L., & Ignatowski, M.
- Venue
- Proceedings of the 23rd international symposium on High-performance parallel and distributed computing
- Publisher
- ACM
- Published
- 2014-06-23
- Pages
- 85-98
- OpenAlex
- W1975237352
- Cited by
- 320
- References
- 0
- FWCI
- 36.16
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: TOP-PIM: throughput-oriented programmable processing in memory. https://app.cochat.ai/papers/10.1145/2600212.2600213
- The paper
- Zhang, D., Jayasena, N., Lyashevsky, A., Greathouse, J. L., Xu, L., & Ignatowski, M. (2014). TOP-PIM: throughput-oriented programmable processing in memory. Proceedings of the 23rd international symposium on High-performance parallel and distributed computing. https://doi.org/10.1145/2600212.2600213
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
