Exploring the Versal AI Engine for 3D Gaussian Splatting

TL;DR

This paper evaluates the AMD Versal AI Engine's performance on 3D Gaussian splatting workloads, demonstrating a 226-fold throughput improvement through a novel algorithm that exploits the architecture's spatial and SIMD capabilities.

Contribution

It provides the first comprehensive performance analysis of the Versal AI Engine on 3D Gaussian splatting and introduces a dedicated algorithm to maximize hardware utilization.

Findings

226-fold throughput increase with the proposed method

Efficient processing of matrix multiplications and color computations

Effective pipelined task assignment exploiting spatial parallelism

Abstract

Dataflow-oriented spatial architectures are the emerging paradigm for higher computation performance and efficiency. AMD Versal AI Engine is a commercial spatial architecture consisting of tiles of VLIW processors supporting SIMD operations arranged in a two-dimensional mesh. The architecture requires the explicit design of task assignments and dataflow configurations for each tile to maximize performance, demanding advanced techniques and meticulous design. However, a few works revealed the performance characteristics of the Versal AI Engine through practical workloads. In this work, we provide the comprehensive performance evaluation of the Versal AI Engine using Gaussian feature computation in 3D Gaussian splatting as a practical workload, and we then propose a novel dedicated algorithm to fully exploit the hardware architecture. The computations of 3D Gaussian splatting include matrix multiplications and color computations utilizing high-dimensional spherical harmonic coefficients. These tasks are processed efficiently by leveraging the SIMD capabilities and their instruction-level parallelism. Additionally, pipelined processing is achieved by assigning different tasks to individual cores, thereby fully exploiting the spatial parallelism of AI Engines. The proposed method demonstrated a 226-fold throughput increase in simulation-based evaluation, outperforming a naive approach. These findings provide valuable insights for application development that effectively harnesses the spatial and architectural advantages of AI Engines.