Systolic Array-based Accelerator for Structured State-Space Models

• URL: http://arxiv.org/abs/2507.21394v3
• Date: Thu, 07 Aug 2025 01:52:45 GMT
• Title: Systolic Array-based Accelerator for Structured State-Space Models
• Authors: Shiva Raja, Cansu Demirkiran, Aakash Sarkar, Milos Popovic, Ajay Joshi,
• Abstract summary: State-Space Models (SSMs) process very long data sequences more efficiently than recurrent and Transformer-based models.<n>In this paper, we introduce a specialized hardware accelerator, EpochCore, for accelerating SSMs.<n>EpochCore achieves on average 2000x improvement in performance on LRA datasets compared to a GPU.
• Score: 1.137896937254823
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Sequence modeling is crucial for AI to understand temporal data and detect complex time-dependent patterns. While recurrent neural networks (RNNs), convolutional neural networks (CNNs), and Transformers have advanced in capturing long-range dependencies, they struggle with achieving high accuracy with very long sequences due to limited memory retention (fixed context window). State-Space Models (SSMs) leverage exponentially decaying memory enabling lengthy context window and so they process very long data sequences more efficiently than recurrent and Transformer-based models. Unlike traditional neural models like CNNs and RNNs, SSM-based models require solving differential equations through continuous integration, making training and inference both compute- and memory-intensive on conventional CPUs and GPUs. In this paper we introduce a specialized hardware accelerator, EpochCore, for accelerating SSMs. EpochCore is based on systolic arrays (SAs) and is designed to enhance the energy efficiency and throughput of inference of SSM-based models for long-range sequence tasks. Within the SA, we propose a versatile processing element (PE) called LIMA-PE to perform traditional and specialized MAC operations to support traditional DNNs and SSMs. To complement the EpochCore microarchitecture, we propose a novel dataflow, ProDF, which enables highly efficient execution of SSM-based models. By leveraging the LIMA-PE microarchitecture and ProDF, EpochCore achieves on average 2000x improvement in performance on LRA datasets compared to a GPU and 250x gains in performance and 45x improvement in energy efficiency, over traditional SA-based accelerators (TPU).

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