mlsanityData to 14 Oct 2026

Compute-in-Memory Implementation of State Space Models for Event Sequence Processing

arXiv 2511.13912 ↗First posted 17 Nov 2025eess.SP cs.AI cs.LGcited by 6

Not on the map. Why?

Abstract (arXiv)

State space models (SSMs) have recently emerged as a powerful framework for long sequence processing, outperforming traditional methods on diverse benchmarks. Fundamentally, SSMs can generalize both recurrent and convolutional networks and have been shown to even capture key functions of biological systems. Here we report an approach to implement SSMs in energy-efficient compute-in-memory (CIM) hardware to achieve real-time, event-driven processing. Our work re-parameterizes the model to function with real-valued coefficients and shared decay constants, reducing the complexity of model mapping onto practical hardware systems. By leveraging device dynamics and diagonalized state transition parameters, the state evolution can be natively implemented in crossbar-based CIM systems combined with memristors exhibiting short-term memory effects. Through this algorithm and hardware co-design, we show the proposed system offers both high accuracy and high energy efficiency while supporting fully asynchronous processing for event-based vision and audio tasks.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 29 of its 79 references that are in the map. First posted on arXiv 17 Nov 2025.

Builds on

29 of 79 references.

References · 29

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  3. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,718
  4. 2019CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Featurescited by 2,884
  5. 2021Efficiently Modeling Long Sequences with Structured State Spacescited by 2,465
  6. 2015Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Codingcited by 2,329
  7. 2017Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inferencecited by 1,593
  8. 2024Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Dualitycited by 1,336
  9. 2022Simplified State Space Layers for Sequence Modelingcited by 788
  10. 2020HiPPO: Recurrent Memory with Optimal Polynomial Projectionscited by 605
  11. 2015BinaryConnect: Training Deep Neural Networks with binary weights during propagationscited by 592
  12. 2023Gated Linear Attention Transformers with Hardware-Efficient Trainingcited by 514
17 more
  1. 2023Resurrecting Recurrent Neural Networks for Long Sequencescited by 408
  2. 2024Jamba: A Hybrid Transformer-Mamba Language Modelcited by 401
  3. 2022Spikformer: When Spiking Neural Network Meets Transformercited by 271
  4. 2021Training Spiking Neural Networks Using Lessons From Deep Learningcited by 259
  5. 2019End-to-End Learning of Representations for Asynchronous Event-Based Datacited by 217
  6. 2023SpikeGPT: Generative Pre-trained Language Model with Spiking Neural Networkscited by 88
  7. 2023GET: Group Event Transformer for Event-Based Visioncited by 73
  8. 2020Temporal Binary Representation for Event-Based Action Recognitioncited by 51
  9. 2023Learning Delays in Spiking Neural Networks using Dilated Convolutions with Learnable Spacingscited by 50
  10. 2024Scalable Event-by-event Processing of Neuromorphic Sensory Signals With Deep State-Space Modelscited by 31
  11. 2022Efficient recurrent architectures through activity sparsity and sparse back-propagation through timecited by 26
  12. 2024Scalable MatMul-free Language Modelingcited by 25
  13. 2023Convolutional State Space Models for Long-Range Spatiotemporal Modelingcited by 19
  14. 2024ALERT-Transformer: Bridging Asynchronous and Synchronous Machine Learning for Real-Time Event-based Spatio-Temporal Datacited by 8
  15. 2025Neuromorphic Principles for Efficient Large Language Models on Intel Loihi 2cited by 8
  16. 2023RN-Net: Reservoir Nodes-Enabled Neuromorphic Vision Sensing Networkcited by 3
  17. 2023Bulk-Switching Memristor-based Compute-In-Memory Module for Deep Neural Network Trainingcited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 6.

6 papers.

  1. 2026Low-Latency Event-Based Object Detection with Spatially-Sparse Linear Attentioncited by 3
  2. 2026DepthWeave-KV: Token-Adaptive Cross-Layer Residual Factorization for Long-Context KV Cache Compressioncited by 1
  3. 2026Federated Learning of Spiking Neural Networks under Heterogeneous Temporal Resolutionscited by 0
  4. 2026FreqDepthKV: Frequency-Guided Depth Sharing for Robust KV Cache Compression in Long-Context LLM Inferencecited by 0
  5. 2026Neuromorphic Object Detection: An In-Depth Study and Future Directionscited by 0
  6. 2026REACT: A Fully Spiking State-Space Model for Real-Time Event-Driven Temporal Perceptioncited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024IMSSA: Deploying modern state-space models on memristive in-memory compute hardware
  2. 2024SMR: State Memory Replay for Long Sequence Modeling
  3. 2024SpiDR: A Reconfigurable Digital Compute-in-Memory Spiking Neural Network Accelerator for Event-based Perception
  4. 2024SpikingSSMs: Learning Long Sequences with Sparse and Parallel Spiking State Space Models
  5. 2025Systolic Array-based Accelerator for Structured State-Space Models
  6. 2023Simple Hardware-Efficient Long Convolutions for Sequence Modeling
  7. 2025HPD: Hybrid Projection Decomposition for Robust State Space Models on Analog CIM Hardware
  8. 2024SPikE-SSM: A Sparse, Precise, and Efficient Spiking State Space Model for Long Sequences Learning