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Neuromorphic Hyperdimensional Computing for Efficiently Processing Event-Based Data
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DOI：10.1109/tvlsi.2026.3695182.png)
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Abstract
En 中文
The neuromorphic sensor’s event-based data output offers significant benefits, including minimal data redundancy and exceptional time resolution, which guarantee low power consumption and heightened sensitivity during the data acquisition process. Spiking neural network (SNN), with its inherent event-driven characteristic, is well-suited for processing event-based data, and its spike-based computing mechanism enhances the efficiency of data processing. Recent studies are exploring the integration of brain-inspired hyperdimensional computing (HDC) with SNN to leverage HDC’s advantages, including the low inference and training complexity, aiming to further reduce hardware overhead associated with SNN deployment. However, existing works have not effectively harnessed the information output by SNN during hyperdimensional encoding, leading to considerable area and energy overhead. In this article, an efficient neuromorphic HDC method is proposed, featuring a simplified hyperdimensional encoding approach that considers the temporal dynamics of SNN. In addition, a lightweight accelerator design matching the proposed method is also given. Experimental results show that the proposed accelerator achieves over 50% area reduction and reduces energy consumption by 20%–90%.
Keywords:
Application-specific integrated circuit (ASIC)
field-programmable gate array (FPGA)
hyperdimensional computing (HDC)
neuromorphic computing
spiking neural network (SNN)
Journal
I
IF:
3.1
Papers:
459
Citations:
7.3K