The deployment of deep neural networks (DNNs) on resource-constrained edge devices is frequently hindered by their significant computational and memory requirements. While partitioning and distributing a DNN across multiple devices is a well-established strategy to mitigate this challenge, prior research has largely focused on single-objective optimization, such as minimizing latency or maximizing throughput. This paper challenges that view by reframing DNN partitioning as a multi-objective optimization problem. We argue that in real-world scenarios, a complex trade-off between latency and throughput exists, which is further complicated by network variability. To address this, we introduce ParetoPipe, an open-source framework that leverages Pareto front analysis to systematically identify optimal partitioning strategies that balance these competing objectives. Our contributions are threefold: we benchmark pipeline partitioned inference on a heterogeneous testbed of Raspberry Pis and a GPU-equipped edge server; we identify Pareto-optimal points to analyze the latency-throughput trade-off under varying network conditions; and we release a flexible, open-source framework to facilitate distributed inference and benchmarking. This toolchain features dual communication backends, PyTorch RPC and a custom lightweight implementation, to minimize overhead and support broad experimentation.

翻译：深度神经网络在资源受限的边缘设备上的部署常因其巨大的计算与内存需求而受阻。虽然将深度神经网络分割并分布到多个设备上是一种成熟的缓解策略，但先前的研究主要集中于单目标优化，例如最小化延迟或最大化吞吐量。本文通过将深度神经网络分割重新定义为多目标优化问题，挑战了这一观点。我们认为，在现实场景中，延迟与吞吐量之间存在复杂的权衡，而网络可变性进一步加剧了这种复杂性。为解决此问题，我们提出了ParetoPipe，这是一个利用帕累托前沿分析来系统性地识别平衡这些竞争目标的最优分割策略的开源框架。我们的贡献有三方面：我们在由树莓派和配备GPU的边缘服务器组成的异构测试平台上对流水线分割推理进行了基准测试；我们识别了帕累托最优点，以分析不同网络条件下的延迟-吞吐量权衡；我们发布了一个灵活的开源框架，以促进分布式推理和基准测试。该工具链具有双通信后端——PyTorch RPC和定制的轻量级实现，旨在最小化开销并支持广泛的实验。