FlexNeRFer: A Multi-Dataflow, Adaptive Sparsity-Aware Accelerator for On-Device NeRF Rendering
Journal:
arXiv
Published Date:
May 10, 2025
Abstract
Neural Radiance Fields (NeRF), an AI-driven approach for 3D view
reconstruction, has demonstrated impressive performance, sparking active
research across fields. As a result, a range of advanced NeRF models has
emerged, leading on-device applications to increasingly adopt NeRF for highly
realistic scene reconstructions. With the advent of diverse NeRF models,
NeRF-based applications leverage a variety of NeRF frameworks, creating the
need for hardware capable of efficiently supporting these models. However, GPUs
fail to meet the performance, power, and area (PPA) cost demanded by these
on-device applications, or are specialized for specific NeRF algorithms,
resulting in lower efficiency when applied to other NeRF models. To address
this limitation, in this work, we introduce FlexNeRFer, an energy-efficient
versatile NeRF accelerator. The key components enabling the enhancement of
FlexNeRFer include: i) a flexible network-on-chip (NoC) supporting
multi-dataflow and sparsity on precision-scalable MAC array, and ii) efficient
data storage using an optimal sparsity format based on the sparsity ratio and
precision modes. To evaluate the effectiveness of FlexNeRFer, we performed a
layout implementation using 28nm CMOS technology. Our evaluation shows that
FlexNeRFer achieves 8.2~243.3x speedup and 24.1~520.3x improvement in energy
efficiency over a GPU (i.e., NVIDIA RTX 2080 Ti), while demonstrating 4.2~86.9x
speedup and 2.3~47.5x improvement in energy efficiency compared to a
state-of-the-art NeRF accelerator (i.e., NeuRex).
