Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

The Future of Consumer Edge-AI Computing

Stefanos Laskaridis, Stylianos I. Venieris, Alexandros Kouris, Rui Li, Nicholas D. Lane

TL;DR

The paper addresses the mismatch between the growing compute demands of modern AI and the limited, heterogeneous capabilities of consumer edge devices. It proposes EdgeAI-Hub as a cross-device fabric that unifies shared compute and shared context while enforcing privacy and sustainability, supported by an orchestrator and a hardware-accelerated Hub. The key contributions include a cross-layer architecture, principles for resource sharing and privacy zones, and a roadmap of use-cases and challenges to guide future development. This work offers a blueprint for scalable, private, multi-device AI at the consumer edge, with potential gains in latency, energy efficiency, and lifecycle sustainability.

Abstract

In the last decade, Deep Learning has rapidly infiltrated the consumer end, mainly thanks to hardware acceleration across devices. However, as we look towards the future, it is evident that isolated hardware will be insufficient. Increasingly complex AI tasks demand shared resources, cross-device collaboration, and multiple data types, all without compromising user privacy or quality of experience. To address this, we introduce a novel paradigm centered around EdgeAI-Hub devices, designed to reorganise and optimise compute resources and data access at the consumer edge. To this end, we lay a holistic foundation for the transition from on-device to Edge-AI serving systems in consumer environments, detailing their components, structure, challenges and opportunities.

The Future of Consumer Edge-AI Computing

TL;DR

The paper addresses the mismatch between the growing compute demands of modern AI and the limited, heterogeneous capabilities of consumer edge devices. It proposes EdgeAI-Hub as a cross-device fabric that unifies shared compute and shared context while enforcing privacy and sustainability, supported by an orchestrator and a hardware-accelerated Hub. The key contributions include a cross-layer architecture, principles for resource sharing and privacy zones, and a roadmap of use-cases and challenges to guide future development. This work offers a blueprint for scalable, private, multi-device AI at the consumer edge, with potential gains in latency, energy efficiency, and lifecycle sustainability.

Abstract

In the last decade, Deep Learning has rapidly infiltrated the consumer end, mainly thanks to hardware acceleration across devices. However, as we look towards the future, it is evident that isolated hardware will be insufficient. Increasingly complex AI tasks demand shared resources, cross-device collaboration, and multiple data types, all without compromising user privacy or quality of experience. To address this, we introduce a novel paradigm centered around EdgeAI-Hub devices, designed to reorganise and optimise compute resources and data access at the consumer edge. To this end, we lay a holistic foundation for the transition from on-device to Edge-AI serving systems in consumer environments, detailing their components, structure, challenges and opportunities.
Paper Structure (19 sections, 5 figures, 1 table)

This paper contains 19 sections, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. Deep Learning Trends
  3. The Evolution of ML Compute
  4. The State of Consumer Edge-AI
  5. Devices are islands
  6. Shortcomings of previous paradigms
  7. Consumer Edge-AI 2.0
  8. Shared compute
  9. Shared context
  10. Privacy
  11. Sustainable-AI
  12. Proposed Architecture
  13. Orchestrator
  14. EdgeAI-Hub
  15. Enabling Upcoming Use-Cases
  16. ...and 4 more sections

Figures (5)

  • Figure 1: Evolution of DNNs operations (FLOPs) and hardware throughput (OP/s). Augmented data from desislavov2021compute.
  • Figure 2: Consumer Edge-AI 1.0 paradigms. While not necessarily mutually exclusive, they provide different levels of interaction between the involved entities.
  • Figure 3: Resource allocation for the execution of ML tasks under a specific budget.
  • Figure 4: Privacy and collaboration zones between devices. These can be supported via rights management through device-owner groups and Access-Control Lists.
  • Figure 5: Orchestrator reference design (\ref{['fig:edgeai_ref']}) and EdgeAI-Hub reference stack (\ref{['fig:edgeai_hub']}).