CAMASim: A Comprehensive Simulation Framework for Content-Addressable Memory based Accelerators (2403.03442v2)
Abstract: Content addressable memory (CAM) stands out as an efficient hardware solution for memory-intensive search operations by supporting parallel computation in memory. However, developing a CAM-based accelerator architecture that achieves acceptable accuracy, while minimizing hardware cost and catering to both exact and approximate search, still presents a significant challenge especially when considering a broader spectrum of applications. This complexity stems from CAM's rapid evolution across multiple levels--algorithms, architectures, circuits, and underlying devices. This paper introduces CAMASim, a first comprehensive CAM accelerator simulation framework, emphasizing modularity, flexibility, and generality. CAMASim establishes the detailed design space for CAM-based accelerators, incorporates automated functional simulation for accuracy, and enables hardware performance prediction, by leveraging a circuit-level CAM modeling tool. This work streamlines the design space exploration for CAM-based accelerator, aiding researchers in developing effective CAM-based accelerators for various search-intensive applications.
- G. Karunaratne, et al. Robust high-dimensional memory-augmented neural networks. Nature communications, 12(1):2468, 2021.
- M. Li, et al. imars: an in-memory-computing architecture for recommendation systems. In Proceedings of the 59th ACM/IEEE Design Automation Conference, DAC ’22, page 463–468, New York, NY, USA, 2022. Association for Computing Machinery.
- A. F. Laguna, et al. Seed-and-vote based in-memory accelerator for dna read mapping. In Proceedings of the 39th International Conference on Computer-Aided Design, ICCAD ’20, New York, NY, USA, 2020. Association for Computing Machinery.
- M. Li, et al. Associative memory based experience replay for deep reinforcement learning. In Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design, pages 1–9, 2022.
- S. Li, et al. Nvsim-cam: A circuit-level simulator for emerging nonvolatile memory based content-addressable memory. In 2016 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), pages 1–7, 2016.
- L. Liu, et al. Eva-cam: A circuit/architecture-level evaluation tool for general content addressable memories. In 2022 Design, Automation & Test in Europe Conference & Exhibition (DATE), pages 1173–1176, 2022.
- A. Kazemi, et al. Achieving software-equivalent accuracy for hyperdimensional computing with ferroelectric-based in-memory computing. Scientific Reports, 2022.
- A. F. Laguna, et al. Invited paper: Algorithm/hardware co-design for few-shot learning at the edge. In 2023 IEEE/ACM International Conference On Computer Aided Design (ICCAD). IEEE, 2023.
- K. Ni, et al. Ferroelectric ternary content-addressable memory for one-shot learning. Nature Electronics, 2(11):521–529, 2019.
- R. Zhang, et al. Sky-tcam: Low-power skyrmion-based ternary content addressable memory. IEEE Transactions on Electron Devices, 70(7):3517–3522, 2023.
- X. S. Hu, et al. In-memory computing with associative memories: a cross-layer perspective. In 2021 IEDM. IEEE, 2021.
- G. Pedretti, et al. X-time: An in-memory engine for accelerating machine learning on tabular data with cams. arXiv preprint arXiv:2304.01285, 2023.
- Y. S. Shao, et al. Aladdin: A pre-rtl, power-performance accelerator simulator enabling large design space exploration of customized architectures. ACM SIGARCH Computer Architecture News, 42(3):97–108, 2014.
- X. Dong, et al. Nvsim: A circuit-level performance, energy, and area model for emerging nonvolatile memory. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 31(7):994–1007, 2012.
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