Profiling-Driven Adaptive Distributed Transformer Inference on Embedded Edge Deployment

2026-05-25

AI Summary

This work addresses the performance bottleneck caused by CPU–GPU data staging during distributed Transformer inference on embedded edge devices, which can result in worse performance than single-device execution. The study is the first to identify this bottleneck in integrated GPU architectures and proposes a dynamic execution decision mechanism grounded in real hardware performance profiling. By combining lightweight offline profiling with Segment Means tensor compression and the GLOO communication backend, the system enables runtime-adaptive scheduling on the NVIDIA Jetson Orin Nano. Compared to static full-tensor exchange strategies, the proposed approach reduces end-to-end latency by 65%–77% and cuts energy consumption by 34%–52%.

Abstract

Distributing Transformer inference across embedded edge devices can alleviate individual memory and compute constraints, yet practical benefits on real hardware remain unclear: prior work relies largely on simulations that overlook hardware-specific communication overheads. We present a hardware prototype study on NVIDIA Jetson Orin Nano devices connected over WiFi. Our key finding is that the dominant bottleneck is not just network bandwidth but also the CPU-GPU staging during communication. Because Jetson's integrated GPU architecture lacks the PCIe/NVLink pathway that NCCL requires, all inter-device data communication should be routed through GLOO and staged in CPU memory; an overhead that scales with communication data volume and makes full-tensor exchange slower than single-device inference across the batch sizes for medium sized models such as ViT. We therefore evaluate Prism by combining Segment Means compression with lightweight offline profiling to adaptively select between local and distributed execution at runtime. Experiments show that this strategy reduces latency by 65%-77% and energy consumption by 34%-52% relative to full-tensor exchange in static distributed execution setup, demonstrating that profiling-driven adaptation is essential for practical distributed Transformer inference on embedded hardware.

Problem

• Distributed Transformer Inference
• Embedded Edge Devices
• Communication Overhead
• CPU-GPU Staging
• Hardware Constraints

Innovation

• Profiling-Driven Adaptation
• Distributed Transformer Inference
• Embedded Edge Devices
• CPU-GPU Staging Overhead
• Segment Means Compression