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HyperMetric: Efficient Hyperdimensional Computing With Metric Learning for Robust Edge Intelligence
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DOI：10.1109/TCAD.2025.3579323.png)
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Abstract
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Hyperdimensional computing (HDC) is emerging as an efficient and robust computing paradigm that has strong resilience to various types of errors. The error robustness nature of HDC makes it a good match for error-prone memory systems. However, the mechanisms behind HDC’s robustness are not fully understood. In this work, we propose HyperMetric, a framework to train highly robust and hardware-friendly HDC models. We found that HDC’s error resilience is driven by Hamming distance margin between hypervectors. Based on this, we propose HyperMetric training that is based on metric learning in order to optimize for high robustness. The experiments show that HyperMetric trained HDC models deliver up to $17\times $ larger Hamming distance margin and up to 14.3% accuracy gain. We accelerate HyperMetric trained models using ReRAM. As compared to state-of-the-art HDC algorithms OnlineHD and HyDREA, HyperMetric ReRAM accelerator is >20% more accurate for computing-in-memory (CIM) errors and >10% more accurate for bit errors even in the face of variations. Furthermore, HyperMetric hardware is 35% more accurate in comparison with existing tinyHD and GENERIC accelerators in the face of $3\times $ ReRAM resistance variance, and 20% more accurate with bit error rate (BER) of up to 20% due to voltage scaling while keeping a good balance between area, power, and processing latency.
Keywords:
Error correction
hardware robustness
hyperdimensional computing (HDC)
metric learning
reliable computation
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