Hyperdimensional computing (HDC) is a promising approach for energy-efficient edge machine learning (ML), where low latency, low power, and tight memory budgets are essential. However, traditional HDC relies on symbolic binding and pseudo-random high-dimensional vectors, which require large dimensionality and heuristic updates to reach competitive accuracy, limiting deployment on edge hardware. We introduce XL-HD, a deterministic, projection-based, fully learnable HDC framework tailored for in-memory acceleration within edge computing systems. The method uses a fixed Sobol sequence to project binary inputs, extending learning beyond conventional HDC. During training, class prototypes are optimized in real-valued space and later binarized, enabling an entirely binary dot-product inference pipeline ideal for IMC hardware such as ReRAM crossbars. XL-HD achieves competitive accuracy on MNIST, UCIHAR, and ISOLET while maintaining a compact IMC-based inference engine with $0.395 \ \text{mm}^2$ area and only $0.40 \ μ\text{J}$ per single-cycle inference.

翻译：超维计算是一种有前景的能效边缘机器学习方法，适用于对低延迟、低功耗和紧凑内存预算要求严苛的场景。然而，传统超维计算依赖符号绑定和伪随机高维向量，需采用高维度空间和启发式更新才能达到竞争性精度，这限制了其在边缘硬件上的部署。我们提出XL-HD——一种面向边缘计算系统中内存加速的确定性、基于投影的完全可学习超维计算框架。该方法采用固定的Sobol序列投影二元输入，将学习能力扩展至传统超维计算之外。训练过程中，类原型在实值空间优化后经二值化处理，实现完全面向二元点积推理的流水线，适用于如ReRAM交叉阵列等内存计算硬件。在保持紧凑的内存计算推理引擎（面积$0.395 \ \text{mm}^2$，单周期推理仅消耗$0.40 \ μ\text{J}$）的前提下，XL-HD在MNIST、UCIHAR和ISOLET数据集上均取得了具有竞争力的精度。