Distribution-Aware Hadamard Quantization for Hardware-Efficient Implicit Neural Representations

TL;DR

This paper introduces DHQ, a distribution-aware Hadamard quantization method for implicit neural representations that effectively quantizes both weights and activations, leading to significant hardware efficiency improvements.

Contribution

The paper proposes a novel distribution-aware Hadamard quantization scheme for INRs that standardizes diverse layer distributions, enabling effective quantization of weights and activations with hardware benefits.

Findings

Reduces latency by 32.7%

Decreases energy consumption by 40.1%

Up to 98.3% resource reduction

Abstract

Implicit Neural Representations (INRs) encode discrete signals using Multi-Layer Perceptrons (MLPs) with complex activation functions. While INRs achieve superior performance, they depend on full-precision number representation for accurate computation, resulting in significant hardware overhead. Previous INR quantization approaches have primarily focused on weight quantization, offering only limited hardware savings due to the lack of activation quantization. To fully exploit the hardware benefits of quantization, we propose DHQ, a novel distribution-aware Hadamard quantization scheme that targets both weights and activations in INRs. Our analysis shows that the weights in the first and last layers have distributions distinct from those in the intermediate layers, while the activations in the last layer differ significantly from those in the preceding layers. Instead of customizing quantizers individually, we utilize the Hadamard transformation to standardize these diverse distributions into a unified bell-shaped form, supported by both empirical evidence and theoretical analysis, before applying a standard quantizer. To demonstrate the practical advantages of our approach, we present an FPGA implementation of DHQ that highlights its hardware efficiency. Experiments on diverse image reconstruction tasks show that DHQ outperforms previous quantization methods, reducing latency by 32.7\%, energy consumption by 40.1\%, and resource utilization by up to 98.3\% compared to full-precision counterparts.