Compiler-Driven Approximation Tuning for Hyperdimensional Computing

2026-06-24

AI Summary

Hyperdimensional computing (HDC) exhibits inherent resilience to approximation but lacks systematic software-hardware co-designed approximation optimization methodologies. This work proposes ApproxHDC, a framework that introduces compiler-driven automated approximation tuning to HDC for the first time. By extending the existing HPVM-HDC compiler infrastructure, ApproxHDC enables retargetable compilation for diverse hardware platforms, including CPUs, GPUs, and in-memory computing devices such as ReRAM and PCM. The framework integrates efficient search and analytical algorithms to jointly explore the approximation configuration space across software and hardware layers, automatically identifying and deploying high-benefit strategies. Experimental results demonstrate that ApproxHDC significantly enhances performance and energy efficiency of HDC workloads on heterogeneous hardware while incurring minimal accuracy loss, thereby validating its effectiveness and generality.

Abstract

As Moore's law reaches its physical and economic limits, domain-specific approaches are increasingly employed to accelerate machine learning workloads. Hyperdimensional Computing (HDC) represents one such emerging paradigm, offering an alternative to conventional deep learning techniques. Rooted in cognitive models of computation, HDC is designed bottom-up with hardware efficiency as a first-class objective. HDC workloads map naturally to heterogeneous hardware platforms, including CPUs, GPUs, and FPGAs, as well as emerging in-memory computing technologies such as Resistive RAM (ReRAM) and Phase-Change Memory (PCM). HDC algorithms are intrinsically tolerant to noise and approximation, enabling substantial performance gains with minimal accuracy loss. In this work, we introduce ApproxHDC, a framework for automated identification and application of domain-specific approximations in HDC workloads. ApproxHDC extends the HPVM-HDC compiler infrastructure to enable retargetable compilation across diverse hardware backends, including CPUs, GPUs, and simulated ReRAM and PCM-based accelerators. The space of possible approximations is exponentially large; ApproxHDC employs efficient search and analysis to navigate it and identify high-impact configurations spanning both software and hardware levels.

Problem

• Hyperdimensional Computing
• Approximation
• Compiler
• Hardware Acceleration
• Domain-Specific Optimization

Innovation

• Hyperdimensional Computing
• Compiler-Driven Approximation
• In-Memory Computing
• Approximation Tuning
• Domain-Specific Optimization