mlsanityData to 14 Oct 2026

Ramulator 2.0: A Modern, Modular, and Extensible DRAM Simulator

arXiv 2308.11030 ↗First posted 21 Aug 2023cs.AR cs.CRcited by 30

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Abstract (arXiv)

We present Ramulator 2.0, a highly modular and extensible DRAM simulator that enables rapid and agile implementation and evaluation of design changes in the memory controller and DRAM to meet the increasing research effort in improving the performance, security, and reliability of memory systems. Ramulator 2.0 abstracts and models key components in a DRAM-based memory system and their interactions into shared interfaces and independent implementations. Doing so enables easy modification and extension of the modeled functions of the memory controller and DRAM in Ramulator 2.0. The DRAM specification syntax of Ramulator 2.0 is concise and human-readable, facilitating easy modifications and extensions. Ramulator 2.0 implements a library of reusable templated lambda functions to model the functionalities of DRAM commands to simplify the implementation of new DRAM standards, including DDR5, LPDDR5, HBM3, and GDDR6. We showcase Ramulator 2.0's modularity and extensibility by implementing and evaluating a wide variety of RowHammer mitigation techniques that require different memory controller design changes. These techniques are added modularly as separate implementations without changing any code in the baseline memory controller implementation. Ramulator 2.0 is rigorously validated and maintains a fast simulation speed compared to existing cycle-accurate DRAM simulators. Ramulator 2.0 is open-sourced under the permissive MIT license at https://github.com/CMU-SAFARI/ramulator2

Key facts. Cited by 30 papers in the map (data through 14 Oct 2026). First posted on arXiv 21 Aug 2023.

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Citing papers per year: 2024: 3; 2025: 9; 2026: 18.

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  1. 2024Duplex: A Device for Large Language Models with Mixture of Experts, Grouped Query Attention, and Continuous Batchingcited by 34
  2. 2025PIM Is All You Need: A CXL-Enabled GPU-Free System for Large Language Model Inferencecited by 22
  3. 2025PAPI: Exploiting Dynamic Parallelism in Large Language Model Decoding with a Processing-In-Memory-Enabled Computing Systemcited by 20
  4. 2024MARCA: Mamba Accelerator with ReConfigurable Architecturecited by 13
  5. 2025Pimba: A Processing-in-Memory Acceleration for Post-Transformer Large Language Model Servingcited by 7
  6. 2025Sparse Attention Remapping with Clustering for Efficient LLM Decoding on PIMcited by 3
  7. 2026NPU Design for Diffusion Language Model Inferencecited by 3
  8. 2025A3D-MoE: Acceleration of Large Language Models with Mixture of Experts via 3D Heterogeneous Integrationcited by 2
  9. 2026AQPIM: Breaking the PIM Capacity Wall for LLMs with In-Memory Activation Quantizationcited by 2
  10. 2024PIFS-Rec: Process-In-Fabric-Switch for Large-Scale Recommendation System Inferencescited by 1
  11. 2025BitStopper: An Efficient Transformer Attention Accelerator via Stage-fusion and Early Terminationcited by 1
  12. 2026LOCALUT: Harnessing Capacity-Computation Tradeoffs for LUT-Based Inference in DRAM-PIMcited by 1
18 more
  1. 2026DSTAR: Accelerating Diffusion Transformers via Spatial and Temporal Redundancy Reductioncited by 1
  2. 2025FuseFlow: A Fusion-Centric Compilation Framework for Sparse Deep Learning on Streaming Dataflowcited by 0
  3. 2025Streaming Tensor Programs: A Streaming Abstraction for Dynamic Parallelismcited by 0
  4. 2025DCC: Data-Centric Compilation of Machine Learning Kernels for Processing-In-Memory Architecturescited by 0
  5. 2026A Scheduling Framework for Efficient MoE Inference on Edge GPU-NDP Systemscited by 0
  6. 2026From Buffers to Registers: Unlocking Fine-Grained FlashAttention with Hybrid-Bonded 3D NPU Co-Designcited by 0
  7. 2026Hardware-Software Co-design for 3D-DRAM-based LLM Serving Acceleratorcited by 0
  8. 2026ELMoE-3D: Leveraging Intrinsic Elasticity of MoE for Hybrid-Bonding-Enabled Self-Speculative Decoding in On-Premises Servingcited by 0
  9. 2026Sieve: Dynamic Expert-Aware PIM Acceleration for Evolving Mixture-of-Experts Modelscited by 0
  10. 2026ORBIS: Output-Guided Token Reduction with Distribution-Aware Matching for Video Diffusion Accelerationcited by 0
  11. 2026Beyond Prefill-Decode Disaggregation: Dissecting LLM Inference for Heterogeneous Platforms via Dynamic Operator Schedulingcited by 0
  12. 2026APEX: Adaptive Expert Prefetching for Memory-Efficient Edge MoE Inferencecited by 0
  13. 2026HYDRA: A Heterogeneous Chiplet DSE Framework for Serving Dynamic Hybrid LLM Workloadscited by 0
  14. 2026NOVA: Technology-Architecture Co-Design of Near-Memory Processing for Attention-SSM-MoE Hybrid LLM Inferencecited by 0
  15. 2026FLINT: Efficiently Leveraging High Bandwidth Flash for Capacity-Scalable LLM Inference Accelerationcited by 0
  16. 2026FlexPosit: Tunable Fractional Precision for LLM Inference Acceleratorscited by 0
  17. 2026SAGE: Semantic-Aware Geographic Error Recovery for AI Data Movementcited by 0
  18. 2026MiX: Micro-Inverted-Scaling for End-to-End Low-Bit Vision-Language Model Accelerationcited by 0