一句话：提出硬件感知的静态分析优化框架 Heim，为 BSC 超维计算推导满足精度目标的最小超向量尺寸。
BSC 超维计算（HDC）是一种高容错近似计算范式，超向量尺寸直接影响精度与资源开销，动态调参耗时且无精度保证。本文提出 Heim，硬件感知的静态分析与优化框架：它解析推导出满足目标精度所需的最小超向量尺寸，并保证硬件错误下仍按期望收敛到精度目标；方法统一神经科学理论以优化 HD 计算。在 25 个基准上，针对 99% 精度要求，Heim 优化计算的中位精度达 99.2%–100.0%，较动态调参最高提升 49.5%，超向量尺寸减少 1.15–7.14 倍，参数化速度提升 30.0–100167.4 倍。并评估了 CAM 与多比特 ReRAM 的等精度收益。
Binary spatter code (BSC)-based hyperdimensional computing (HDC) is a highly error-resilient approximate computational paradigm suited for error-prone, emerging hardware platforms. In BSC HDC, the basic datatype is a hypervector , a typically large binary vector, where the size of the hypervector has a significant impact on the fidelity and resource usage of the computation. Typically, the hypervector size is dynamically tuned to deliver the desired accuracy; this process is time-consuming and often produces hypervector sizes that lack accuracy guarantees and produce poor results when reused for very similar workloads. We present Heim, a hardware-aware static analysis and optimization framework for BSC HD computations. Heim analytically derives the minimum hypervector size that minimizes resource usage and meets the target accuracy requirement. Heim guarantees the optimized computation converges to the user-provided accuracy target on expectation, even in the presence of hardware error. Heim deploys a novel static analysis procedure that unifies theoretical results from the neuroscience community to systematically optimize HD computations. We evaluate Heim against dynamic tuning-based optimization on 25 benchmark data structures. Given a 99% accuracy requirement, Heim-optimized computations achieve a 99.2%-100.0% median accuracy, up to 49.5% higher than dynamic tuning-based optimization, while achieving 1.15x-7.14x reductions in hypervector size compared to HD computations that achieve comparable query accuracy and finding parametrizations 30.0x-100167.4x faster than dynamic tuning-based approaches. We also use Heim to systematically evaluate the performance benefits of using analog CAMs and multiple-bit-per-cell ReRAM over conventional hardware, while maintaining iso-accuracy – for both emerging technologies, we find usages where the emerging hardware imparts significant benefits.
@article{paperbot1852,
title = {Hardware-Aware Static Optimization of Hyperdimensional Computations},
author = {Pu (Luke) Yi and Sara Achour},
journal = {Proceedings of the ACM on Programming Languages},
volume = {7},
number = {OOPSLA2},
year = {2023},
doi = {10.1145/3622797}
}