Machine learning based on neural networks has advanced rapidly, but the high energy consumption required for training and inference remains a major challenge. Hyperdimensional Computing (HDC) offers a lightweight, brain-inspired alternative that enables high parallelism but often suffers from lower accuracy on complex visual tasks. To overcome this, hybrid accelerators combining HDC and Convolutional Neural Networks (CNNs) have been proposed, though their adoption is limited by poor generalizability and programmability. The rise of open-source RISC-V architectures has created new opportunities for domain-specific GPU design. Unlike traditional proprietary GPUs, emerging RISC-V-based GPUs provide flexible, programmable platforms suitable for custom computation models such as HDC. In this study, we design and implement custom GPU instructions optimized for HDC operations, enabling efficient processing for hybrid HDC-CNN workloads. Experimental results using four types of custom HDC instructions show a performance improvement of up to 56.2 times in microbenchmark tests, demonstrating the potential of RISC-V GPUs for energy-efficient, high-performance computing.

翻译：基于神经网络的机器学习发展迅速，但训练和推理所需的高能耗仍是主要挑战。超维计算（HDC）作为一种轻量级、受大脑启发的替代方案，具备高并行性优势，但在复杂视觉任务中常面临精度较低的问题。为克服这一局限，结合HDC与卷积神经网络（CNN）的混合加速器被提出，但其应用受限于泛化能力不足和可编程性差。开源RISC-V架构的兴起为领域专用GPU设计创造了新机遇。与传统专有GPU不同，新兴的基于RISC-V的GPU为HDC等定制计算模型提供了灵活可编程的平台。本研究设计并实现了针对HDC运算优化的自定义GPU指令，使混合HDC-CNN工作负载得以高效处理。采用四类自定义HDC指令的微基准测试显示性能最高提升56.2倍，证明了RISC-V GPU在能效与高性能计算方面的潜力。