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KAITIAN: A Unified Communication Framework for Enabling Efficient Collaboration Across Heterogeneous Accelerators in Embodied AI Systems

Jieke Lin, Wanyu Wang, Longxiang Yin, Yinhe Han

TL;DR

KAITIAN tackles interoperability barriers in embodied AI by enabling distributed training across heterogeneous accelerators (GPUs, NPUs, FPGAs). It provides a unified hybrid communication framework that uses vendor-optimized intra-group libraries for fast within-group communication and a general-purpose inter-group backend for cross-group data exchange, coordinated by a PyTorch extension. The framework includes a load-adaptive scheduling mechanism that aligns per-device workload with real-time performance estimates to maximize throughput. Experimental results on a mixed GPU-MLU testbed show up to 42% training time reduction with minimal overhead and preserved accuracy, demonstrating the practicality of heterogeneous collaboration for embodied AI.

Abstract

Embodied Artificial Intelligence (AI) systems, such as autonomous robots and intelligent vehicles, are increasingly reliant on diverse heterogeneous accelerators (e.g., GPGPUs, NPUs, FPGAs) to meet stringent real-time processing and energy-efficiency demands. However, the proliferation of vendor-specific proprietary communication libraries creates significant interoperability barriers, hindering seamless collaboration between different accelerator types and leading to suboptimal resource utilization and performance bottlenecks in distributed AI workloads. This paper introduces KAITIAN, a novel distributed communication framework designed to bridge this gap. KAITIAN provides a unified abstraction layer that intelligently integrates vendor-optimized communication libraries for intra-group efficiency with general-purpose communication protocols for inter-group interoperability. Crucially, it incorporates a load-adaptive scheduling mechanism that dynamically balances computational tasks across heterogeneous devices based on their real-time performance characteristics. Implemented as an extension to PyTorch and rigorously evaluated on a testbed featuring NVIDIA GPUs and Cambricon MLUs, KAITIAN demonstrates significant improvements in resource utilization and scalability for distributed training tasks. Experimental results show that KAITIAN can accelerate training time by up to 42% compared to baseline homogeneous systems, while incurring minimal communication overhead (2.8--4.3%) and maintaining model accuracy. KAITIAN paves the way for more flexible and powerful heterogeneous computing in complex embodied AI applications.

KAITIAN: A Unified Communication Framework for Enabling Efficient Collaboration Across Heterogeneous Accelerators in Embodied AI Systems

TL;DR

KAITIAN tackles interoperability barriers in embodied AI by enabling distributed training across heterogeneous accelerators (GPUs, NPUs, FPGAs). It provides a unified hybrid communication framework that uses vendor-optimized intra-group libraries for fast within-group communication and a general-purpose inter-group backend for cross-group data exchange, coordinated by a PyTorch extension. The framework includes a load-adaptive scheduling mechanism that aligns per-device workload with real-time performance estimates to maximize throughput. Experimental results on a mixed GPU-MLU testbed show up to 42% training time reduction with minimal overhead and preserved accuracy, demonstrating the practicality of heterogeneous collaboration for embodied AI.

Abstract

Embodied Artificial Intelligence (AI) systems, such as autonomous robots and intelligent vehicles, are increasingly reliant on diverse heterogeneous accelerators (e.g., GPGPUs, NPUs, FPGAs) to meet stringent real-time processing and energy-efficiency demands. However, the proliferation of vendor-specific proprietary communication libraries creates significant interoperability barriers, hindering seamless collaboration between different accelerator types and leading to suboptimal resource utilization and performance bottlenecks in distributed AI workloads. This paper introduces KAITIAN, a novel distributed communication framework designed to bridge this gap. KAITIAN provides a unified abstraction layer that intelligently integrates vendor-optimized communication libraries for intra-group efficiency with general-purpose communication protocols for inter-group interoperability. Crucially, it incorporates a load-adaptive scheduling mechanism that dynamically balances computational tasks across heterogeneous devices based on their real-time performance characteristics. Implemented as an extension to PyTorch and rigorously evaluated on a testbed featuring NVIDIA GPUs and Cambricon MLUs, KAITIAN demonstrates significant improvements in resource utilization and scalability for distributed training tasks. Experimental results show that KAITIAN can accelerate training time by up to 42% compared to baseline homogeneous systems, while incurring minimal communication overhead (2.8--4.3%) and maintaining model accuracy. KAITIAN paves the way for more flexible and powerful heterogeneous computing in complex embodied AI applications.
Paper Structure (23 sections, 4 figures)

This paper contains 23 sections, 4 figures.

Table of Contents

  1. Introduction
  2. Related Work
  3. Communication Libraries and Frameworks
  4. Load Balancing in Heterogeneous Systems
  5. The KAITIAN Framework Design
  6. Hierarchical Communication Management
  7. Hybrid Communication Backend Integration
  8. Load-Adaptive Scheduling Mechanism
  9. System Coordination
  10. Implementation and Evaluation
  11. Implementation Details
  12. Experimental Setup
  13. Evaluation Metrics
  14. Results and Analysis
  15. Training Efficiency and Accuracy
  16. ...and 8 more sections

Figures (4)

  • Figure 1: KAITIAN Framework Integration with PyTorch.
  • Figure 2: KAITIAN Training Efficiency (Time to Complete 50 Epochs) and Model Accuracy Comparison on CIFAR-10 with MobileNetV2. Lower training time is better. Accuracy is Top-1 on the test set.
  • Figure 3: Impact of Load Adaptive Mechanism on Heterogeneous Training (e.g., 1G+1M configuration). Strategy A might be naive equal batch splitting, Strategy B KAITIAN's adaptive splitting, Strategy C a suboptimal fixed ratio. The y-axis likely represents training time per epoch or overall, and x-axis different strategies or device utilization ratios. KAITIAN (Strategy B) finds a balance that minimizes training time by ensuring both GPU and MLU are effectively utilized according to their capabilities.
  • Figure 4: Communication Overhead of KAITIAN in Homogeneous Settings. Comparison of training time using native NCCL/CNCL versus KAITIAN managing the same homogeneous devices.