DAOP: Data-Aware Offloading and Predictive Pre-Calculation for Efficient MoE Inference

TL;DR

DAOP is a novel on-device MoE inference engine that dynamically allocates experts between CPU and GPU, using predictive pre-calculation to reduce data transfer latency and improve efficiency on memory-constrained devices.

Contribution

It introduces a dynamic expert allocation and predictive pre-calculation mechanism for efficient MoE inference on resource-limited devices.

Findings

DAOP outperforms traditional caching and prefetching methods by up to 8.20x.

DAOP achieves 1.35x better performance than offloading techniques.

It maintains model accuracy through a graceful degradation mechanism.

Abstract

Mixture-of-Experts (MoE) models, though highly effective for various machine learning tasks, face significant deployment challenges on memory-constrained devices. While GPUs offer fast inference, their limited memory compared to CPUs means not all experts can be stored on the GPU simultaneously, necessitating frequent, costly data transfers from CPU memory, often negating GPU speed advantages. To address this, we present DAOP, an on-device MoE inference engine to optimize parallel GPU-CPU execution. DAOP dynamically allocates experts between CPU and GPU based on per-sequence activation patterns, and selectively pre-calculates predicted experts on CPUs to minimize transfer latency. This approach enables efficient resource utilization across various expert cache ratios while maintaining model accuracy through a novel graceful degradation mechanism. Comprehensive evaluations across various datasets show that DAOP outperforms traditional expert caching and prefetching methods by up to 8.20x and offloading techniques by 1.35x while maintaining accuracy.