Robust Reasoning and Learning with Brain-Inspired Representations under Hardware-Induced Nonlinearities

TL;DR

This paper presents a hardware-aware optimization framework for Hyperdimensional Computing that compensates for nonlinear distortions in compute-in-memory architectures, significantly improving accuracy and robustness.

Contribution

It introduces an end-to-end calibration method that minimizes hardware-induced errors in HDC, enabling scalable, energy-efficient reasoning on CIM hardware.

Findings

Achieves 84% accuracy with severe hardware perturbations, 48% higher than naive methods.

Preserves HDC robustness and symbolic properties under nonlinear hardware conditions.

Improves RelHD accuracy on Cora dataset by 5.4 times in nonlinear environments.

Abstract

Traditional machine learning depends on high-precision arithmetic and near-ideal hardware assumptions, which is increasingly challenged by variability in aggressively scaled semiconductor devices. Compute-in-memory (CIM) architectures alleviate data-movement bottlenecks and improve energy efficiency yet introduce nonlinear distortions and reliability concerns. We address these issues with a hardware-aware optimization framework based on Hyperdimensional Computing (HDC), systematically compensating for non-ideal similarity computations in CIM. Our approach formulates encoding as an optimization problem, minimizing the Frobenius norm between an ideal kernel and its hardware-constrained counterpart, and employs a joint optimization strategy for end-to-end calibration of hypervector representations. Experimental results demonstrate that our method when applied to QuantHD achieves 84\% accuracy under severe hardware-induced perturbations, a 48\% increase over naive QuantHD under the same conditions. Additionally, our optimization is vital for graph-based HDC reliant on precise variable-binding for interpretable reasoning. Our framework preserves the accuracy of RelHD on the Cora dataset, achieving a 5.4$\times$ accuracy improvement over naive RelHD under nonlinear environments. By preserving HDC's robustness and symbolic properties, our solution enables scalable, energy-efficient intelligent systems capable of classification and reasoning on emerging CIM hardware.