# MARCA: Mamba Accelerator with ReConfigurable Architecture

arXiv: https://arxiv.org/abs/2409.11440 · HTML: https://mlsanity.com/paper/2409.11440/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 47 references that are in the map.
- First posted on arXiv 16 Sep 2024.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge](https://mlsanity.com/paper/1803.05457/) (2018)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [HellaSwag: Can a Machine Really Finish Your Sentence?](https://mlsanity.com/paper/1905.07830/) (2019)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [Pointer Sentinel Mixture Models](https://mlsanity.com/paper/1609.07843/) (2016)
- [PIQA: Reasoning about Physical Commonsense in Natural Language](https://mlsanity.com/paper/1911.11641/) (2019)
- [Efficiently Modeling Long Sequences with Structured State Spaces](https://mlsanity.com/paper/2111.00396/) (2021)
- [The LAMBADA dataset: Word prediction requiring a broad discourse context](https://mlsanity.com/paper/1606.06031/) (2016)
- [Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning](https://mlsanity.com/paper/1702.03118/) (2017)
- [Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers](https://mlsanity.com/paper/2110.13985/) (2021)
- [On the Parameterization and Initialization of Diagonal State Space Models](https://mlsanity.com/paper/2206.11893/) (2022)
- [Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2402.00789/) (2024)
- [NVIDIA Tensor Core Programmability, Performance & Precision](https://mlsanity.com/paper/1803.04014/) (2018)
- [Point Mamba: A Novel Point Cloud Backbone Based on State Space Model with Octree-Based Ordering Strategy](https://mlsanity.com/paper/2403.06467/) (2024)
- [Hardware Accelerator for Multi-Head Attention and Position-Wise Feed-Forward in the Transformer](https://mlsanity.com/paper/2009.08605/) (2020)
- [Ramulator 2.0: A Modern, Modular, and Extensible DRAM Simulator](https://mlsanity.com/paper/2308.11030/) (2023)
- [Flex-SFU: Accelerating DNN Activation Functions by Non-Uniform Piecewise Approximation](https://mlsanity.com/paper/2305.04546/) (2023)

## Built on by (most cited first, 13 of 13)

- [Large Language Model Inference Acceleration: A Comprehensive Hardware Perspective](https://mlsanity.com/paper/2410.04466/) (2024)
- [LightMamba: Efficient Mamba Acceleration on FPGA with Quantization and Hardware Co-design](https://mlsanity.com/paper/2502.15260/) (2025)
- [Pimba: A Processing-in-Memory Acceleration for Post-Transformer Large Language Model Serving](https://mlsanity.com/paper/2507.10178/) (2025)
- [SpecMamba: Accelerating Mamba Inference on FPGA with Speculative Decoding](https://mlsanity.com/paper/2509.19873/) (2025)
- [XAMBA: Enabling Efficient State Space Models on Resource-Constrained Neural Processing Units](https://mlsanity.com/paper/2502.06924/) (2025)
- [FastMamba: A High-Speed and Efficient Mamba Accelerator on FPGA with Accurate Quantization](https://mlsanity.com/paper/2505.18975/) (2025)
- [A Persistent-State Dataflow Accelerator for Memory-Bound Linear Attention Decode on FPGA](https://mlsanity.com/paper/2603.05931/) (2026)
- [eMamba: Efficient Acceleration Framework for Mamba Models in Edge Computing](https://mlsanity.com/paper/2508.10370/) (2025)
- [PerfMamba: Performance Analysis and Pruning of Selective State Space Models](https://mlsanity.com/paper/2511.22849/) (2025)
- [QMC: Efficient SLM Edge Inference via Outlier-Aware Quantization and Emergent Memories Co-Design](https://mlsanity.com/paper/2601.14549/) (2026)
- [Systolic Array-based Accelerator for Structured State-Space Models](https://mlsanity.com/paper/2507.21394/) (2025)
- [ViM-Q: Scalable Algorithm-Hardware Co-Design for Vision Mamba Model Inference on FPGA](https://mlsanity.com/paper/2605.01935/) (2026)
- [HYDRA: A Heterogeneous Chiplet DSE Framework for Serving Dynamic Hybrid LLM Workloads](https://mlsanity.com/paper/2608.19395/) (2026)

## Abstract (arXiv)

We propose a Mamba accelerator with reconfigurable architecture, MARCA.We propose three novel approaches in this paper. (1) Reduction alternative PE array architecture for both linear and element-wise operations. For linear operations, the reduction tree connected to PE arrays is enabled and executes the reduction operation. For element-wise operations, the reduction tree is disabled and the output bypasses. (2) Reusable nonlinear function unit based on the reconfigurable PE. We decompose the exponential function into element-wise operations and a shift operation by a fast biased exponential algorithm, and the activation function (SiLU) into a range detection and element-wise operations by a piecewise approximation algorithm. Thus, the reconfigurable PEs are reused to execute nonlinear functions with negligible accuracy loss.(3) Intra-operation and inter-operation buffer management strategy. We propose intra-operation buffer management strategy to maximize input data sharing for linear operations within operations, and inter-operation strategy for element-wise operations between operations. We conduct extensive experiments on Mamba model families with different sizes.MARCA achieves up to 463.22×/11.66× speedup and up to 9761.42×/242.52× energy efficiency compared to Intel Xeon 8358P CPU and NVIDIA Tesla A100 GPU implementations, respectively.