NOM: Network-On-Memory for Inter-Bank Data Transfer in Highly-Banked Memories
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Abstract (arXiv)
Data copy is a widely-used memory operation in many programs and operating system services. In conventional computers, data copy is often carried out by two separate read and write transactions that pass data back and forth between the DRAM chip and the processor chip. Some prior mechanisms propose to avoid this unnecessary data movement by using the shared internal bus in the DRAM chip to directly copy data within the DRAM chip (e.g., between two DRAM banks). While these methods exhibit superior performance compared to conventional techniques, data copy across different DRAM banks is still greatly slower than data copy within the same DRAM bank. Hence, these techniques have limited benefit for the emerging 3D-stacked memories (e.g., HMC and HBM) that contain hundreds of DRAM banks across multiple memory controllers. In this paper, we present Network-on-Memory (NoM), a lightweight inter-bank data communication scheme that enables direct data copy across both memory banks of a 3D-stacked memory. NoM adopts a TDM-based circuit-switching design, where circuit setup is done by the memory controller. Compared to state-of-the-art approaches, NoM enables both fast data copy between multiple DRAM banks and concurrent data transfer operations. Our evaluation shows that NoM improves the performance of data-intensive workloads by 3.8X and 75%, on average, compared to the baseline conventional 3D-stacked DRAM architecture and state-of-the-art techniques, respectively.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Apr 2020.
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Citing papers per year: 2022: 4; 2023: 0; 2024: 0; 2025: 0; 2026: 1.
5 papers.
- 2022Machine Learning Training on a Real Processing-in-Memory Systemcited by 4
- 2022Accelerating Neural Network Inference with Processing-in-DRAM: From the Edge to the Cloudcited by 4
- 2022An Experimental Evaluation of Machine Learning Training on a Real Processing-in-Memory Systemcited by 2
- 2022Heterogeneous Data-Centric Architectures for Modern Data-Intensive Applications: Case Studies in Machine Learning and Databasescited by 1
- 2026RED-PIM: Reducing Data Movement for Transformers using Processing-in-Memorycited by 0
- 2026RED-PIM: Reducing Data Movement for Transformers using Processing-in-Memory23 Jul 2026
- 2022Accelerating Neural Network Inference with Processing-in-DRAM: From the Edge to the Cloud19 Sep 2022
- 2022An Experimental Evaluation of Machine Learning Training on a Real Processing-in-Memory System16 Jul 2022
- 2022Machine Learning Training on a Real Processing-in-Memory System13 Jun 2022
- 2022Heterogeneous Data-Centric Architectures for Modern Data-Intensive Applications: Case Studies in Machine Learning and Databases29 May 2022