# The Future of Consumer Edge-AI Computing

arXiv: https://arxiv.org/abs/2210.10514 · HTML: https://mlsanity.com/paper/2210.10514/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 57 references that are in the map.
- First posted on arXiv 19 Oct 2022.

## Builds on (most cited first, 30 of 30)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [Machine Unlearning](https://mlsanity.com/paper/1912.03817/) (2019)
- [Federated Multi-Task Learning](https://mlsanity.com/paper/1705.10467/) (2017)
- [Sustainable AI: Environmental Implications, Challenges and Opportunities](https://mlsanity.com/paper/2111.00364/) (2021)
- [FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout](https://mlsanity.com/paper/2102.13451/) (2021)
- [The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink](https://mlsanity.com/paper/2204.05149/) (2022)
- [Distributed Deep Neural Networks over the Cloud, the Edge and End Devices](https://mlsanity.com/paper/1709.01921/) (2017)
- [SPINN: Synergistic Progressive Inference of Neural Networks over Device and Cloud](https://mlsanity.com/paper/2008.06402/) (2020)
- [Adaptive Inference through Early-Exit Networks: Design, Challenges and Directions](https://mlsanity.com/paper/2106.05022/) (2021)
- [Compute and Energy Consumption Trends in Deep Learning Inference](https://mlsanity.com/paper/2109.05472/) (2021)
- [DarkneTZ: Towards Model Privacy at the Edge using Trusted Execution Environments](https://mlsanity.com/paper/2004.05703/) (2020)
- [Orchestra: Unsupervised Federated Learning via Globally Consistent Clustering](https://mlsanity.com/paper/2205.11506/) (2022)
- [CaPC Learning: Confidential and Private Collaborative Learning](https://mlsanity.com/paper/2102.05188/) (2021)
- [Adaptable Butterfly Accelerator for Attention-based NNs via Hardware and Algorithm Co-design](https://mlsanity.com/paper/2209.09570/) (2022)
- [Best of Both Worlds: AutoML Codesign of a CNN and its Hardware Accelerator](https://mlsanity.com/paper/2002.05022/) (2020)
- [HAPI: Hardware-Aware Progressive Inference](https://mlsanity.com/paper/2008.03997/) (2020)
- [Smart at what cost? Characterising Mobile Deep Neural Networks in the wild](https://mlsanity.com/paper/2109.13963/) (2021)
- [Federated Learning with Autotuned Communication-Efficient Secure Aggregation](https://mlsanity.com/paper/1912.00131/) (2019)
- [EmBench: Quantifying Performance Variations of Deep Neural Networks across Modern Commodity Devices](https://mlsanity.com/paper/1905.07346/) (2019)
- [Multi-Exit Semantic Segmentation Networks](https://mlsanity.com/paper/2106.03527/) (2021)
- [All One Needs to Know about Fog Computing and Related Edge Computing Paradigms: A Complete Survey](https://mlsanity.com/paper/1808.05283/) (2018)
- [CookGAN: Meal Image Synthesis from Ingredients](https://mlsanity.com/paper/2002.11493/) (2020)
- [Federated Active Learning (F-AL): an Efficient Annotation Strategy for Federated Learning](https://mlsanity.com/paper/2202.00195/) (2022)
- [Multi-DNN Accelerators for Next-Generation AI Systems](https://mlsanity.com/paper/2205.09376/) (2022)

## Built on by (most cited first, 12 of 12)

- [MELTing point: Mobile Evaluation of Language Transformers](https://mlsanity.com/paper/2403.12844/) (2024)
- [Semantic Successive Refinement: A Generative AI-aided Semantic Communication Framework](https://mlsanity.com/paper/2408.05112/) (2024)
- [Maestro: Uncovering Low-Rank Structures via Trainable Decomposition](https://mlsanity.com/paper/2308.14929/) (2023)
- [lm-Meter: Unveiling Runtime Inference Latency for On-Device Language Models](https://mlsanity.com/paper/2510.06126/) (2025)
- [MultiTASC: A Multi-Tenancy-Aware Scheduler for Cascaded DNN Inference at the Consumer Edge](https://mlsanity.com/paper/2306.12830/) (2023)
- [FedAli: Personalized Federated Learning Alignment with Prototype Layers for Generalized Mobile Services](https://mlsanity.com/paper/2411.10595/) (2024)
- [Smaller, Smarter, Closer: The Edge of Collaborative Generative AI](https://mlsanity.com/paper/2505.16499/) (2025)
- [DecoHD: Decomposed Hyperdimensional Classification under Extreme Memory Budgets](https://mlsanity.com/paper/2511.03911/) (2025)
- [AdaBet: Gradient-free Layer Selection for Efficient Training of Deep Neural Networks](https://mlsanity.com/paper/2510.03101/) (2025)
- [Profiling AI Models: Towards Efficient Computation Offloading in Heterogeneous Edge AI Systems](https://mlsanity.com/paper/2411.00859/) (2024)
- [MultiTASC++: A Continuously Adaptive Scheduler for Edge-Based Multi-Device Cascade Inference](https://mlsanity.com/paper/2412.04147/) (2024)
- [MANE: A Multi-Path Adaptive Network for Edge Onloading of Deep Neural Networks](https://mlsanity.com/paper/2609.14660/) (2026)

## Abstract (arXiv)

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.