SEIFER: Scalable Edge Inference for Deep Neural Networks

TL;DR

SEIFER is a scalable framework that enables efficient, fault-tolerant deployment of deep neural networks across distributed edge devices using Kubernetes, significantly improving inference throughput.

Contribution

It introduces a novel edge inference framework that partitions and distributes DNNs over resource-constrained edge networks with fault tolerance and automatic updates.

Findings

Inference throughput improved by 200% with sufficient nodes

Framework supports fault tolerance and automatic model updates

Open-source implementation available for research community

Abstract

Edge inference is becoming ever prevalent through its applications from retail to wearable technology. Clusters of networked resource-constrained edge devices are becoming common, yet there is no production-ready orchestration system for deploying deep learning models over such edge networks which adopts the robustness and scalability of the cloud. We present SEIFER, a framework utilizing a standalone Kubernetes cluster to partition a given DNN and place these partitions in a distributed manner across an edge network, with the goal of maximizing inference throughput. The system is node fault-tolerant and automatically updates deployments based on updates to the model's version. We provide a preliminary evaluation of a partitioning and placement algorithm that works within this framework, and show that we can improve the inference pipeline throughput by 200% by utilizing sufficient numbers of resource-constrained nodes. We have implemented SEIFER in open-source software that is publicly available to the research community.