DRAM Bender: An Extensible and Versatile FPGA-based Infrastructure to Easily Test State-of-the-art DRAM Chips
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Abstract (arXiv)
To understand and improve DRAM performance, reliability, security and energy efficiency, prior works study characteristics of commodity DRAM chips. Unfortunately, state-of-the-art open source infrastructures capable of conducting such studies are obsolete, poorly supported, or difficult to use, or their inflexibility limit the types of studies they can conduct. We propose DRAM Bender, a new FPGA-based infrastructure that enables experimental studies on state-of-the-art DRAM chips. DRAM Bender offers three key features at the same time. First, DRAM Bender enables directly interfacing with a DRAM chip through its low-level interface. This allows users to issue DRAM commands in arbitrary order and with finer-grained time intervals compared to other open source infrastructures. Second, DRAM Bender exposes easy-to-use C++ and Python programming interfaces, allowing users to quickly and easily develop different types of DRAM experiments. Third, DRAM Bender is easily extensible. The modular design of DRAM Bender allows extending it to (i) support existing and emerging DRAM interfaces, and (ii) run on new commercial or custom FPGA boards with little effort. To demonstrate that DRAM Bender is a versatile infrastructure, we conduct three case studies, two of which lead to new observations about the DRAM RowHammer vulnerability. In particular, we show that data patterns supported by DRAM Bender uncovers a larger set of bit-flips on a victim row compared to the data patterns commonly used by prior work. We demonstrate the extensibility of DRAM Bender by implementing it on five different FPGAs with DDR4 and DDR3 support. DRAM Bender is freely and openly available at https://github.com/CMU-SAFARI/DRAM-Bender.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 Nov 2022.
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Citing papers per year: 2025: 3; 2026: 1.
4 papers.
- 2025MVDRAM: Enabling GeMV Execution in Unmodified DRAM for Low-Bit LLM Accelerationcited by 0
- 2025Remote Rowhammer Attack using Adversarial Observations on Federated Learning Clientscited by 0
- 2025EIM-TRNG: Obfuscating Deep Neural Network Weights with Encoding-in-Memory True Random Number Generator via RowHammercited by 0
- 2026DARTH-PUM: A Hybrid Processing-Using-Memory Architecturecited by 0
- 2026DARTH-PUM: A Hybrid Processing-Using-Memory Architecture17 Feb 2026
- 2025EIM-TRNG: Obfuscating Deep Neural Network Weights with Encoding-in-Memory True Random Number Generator via RowHammer3 Jul 2025
- 2025Remote Rowhammer Attack using Adversarial Observations on Federated Learning Clients9 May 2025
- 2025MVDRAM: Enabling GeMV Execution in Unmodified DRAM for Low-Bit LLM Acceleration31 Mar 2025