Which Design Decisions in AI-enabled Mobile Applications Contribute to Greener AI?

• URL: http://arxiv.org/abs/2109.15284v2
• Date: Tue, 30 May 2023 09:42:13 GMT
• Title: Which Design Decisions in AI-enabled Mobile Applications Contribute to Greener AI?
• Authors: Roger Creus Castanyer and Silverio Mart\'inez-Fern\'andez and Xavier Franch
• Abstract summary: This report consists of a plan to conduct an empirical study to quantify the implications of the design decisions on AI-enabled applications performance. We will implement both image-based and language-based neural networks in mobile applications to solve multiple image classification and text classification problems.
• Score: 7.194465440864905
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Background: The construction, evolution and usage of complex artificial intelligence (AI) models demand expensive computational resources. While currently available high-performance computing environments support well this complexity, the deployment of AI models in mobile devices, which is an increasing trend, is challenging. Mobile applications consist of environments with low computational resources and hence imply limitations in the design decisions during the AI-enabled software engineering lifecycle that balance the trade-off between the accuracy and the complexity of the mobile applications. Objective: Our objective is to systematically assess the trade-off between accuracy and complexity when deploying complex AI models (e.g. neural networks) to mobile devices, which have an implicit resource limitation. We aim to cover (i) the impact of the design decisions on the achievement of high-accuracy and low resource-consumption implementations; and (ii) the validation of profiling tools for systematically promoting greener AI. Method: This confirmatory registered report consists of a plan to conduct an empirical study to quantify the implications of the design decisions on AI-enabled applications performance and to report experiences of the end-to-end AI-enabled software engineering lifecycle. Concretely, we will implement both image-based and language-based neural networks in mobile applications to solve multiple image classification and text classification problems on different benchmark datasets. Overall, we plan to model the accuracy and complexity of AI-enabled applications in operation with respect to their design decisions and will provide tools for allowing practitioners to gain consciousness of the quantitative relationship between the design decisions and the green characteristics of study.

Related papers

• CODEI: Resource-Efficient Task-Driven Co-Design of Perception and Decision Making for Mobile Robots Applied to Autonomous Vehicles [7.480009220235756]
  This paper focuses on the integration challenges and strategies for designing mobile robots. We emphasize the interplay between perception and motion planning in decision-making. We refer to this framework for solving the co-design problem of mobile robots as CODEI, short for Co-design of Embodied Intelligence.
  arXiv  Detail & Related papers  (2025-03-13T12:12:44Z)
• Empowering Edge Intelligence: A Comprehensive Survey on On-Device AI Models [16.16798813072285]
  The rapid advancement of artificial intelligence (AI) technologies has led to an increasing deployment of AI models on edge and terminal devices. This survey comprehensively explores the current state, technical challenges, and future trends of on-device AI models.
  arXiv  Detail & Related papers  (2025-03-08T02:59:51Z)
• Collaborative AI in Sentiment Analysis: System Architecture, Data Prediction and Deployment Strategies [3.3374611485861116]
  Large language model (LLM) based artificial intelligence technologies have been a game-changer, particularly in sentiment analysis. However, integrating diverse AI models for processing complex multimodal data and the associated high costs of feature extraction presents significant challenges. This study introduces a collaborative AI framework designed to efficiently distribute and resolve tasks across various AI systems.
  arXiv  Detail & Related papers  (2024-10-17T06:14:34Z)
• Uncertainty Estimation in Multi-Agent Distributed Learning for AI-Enabled Edge Devices [0.0]
  Edge IoT devices have seen a paradigm shift with the introduction of FPGAs and AI accelerators. This advancement has vastly amplified their computational capabilities, emphasizing the practicality of edge AI. Our study explores methods that enable distributed data processing through AI-enabled edge devices, enhancing collaborative learning capabilities.
  arXiv  Detail & Related papers  (2024-03-14T07:40:32Z)
• Using the Abstract Computer Architecture Description Language to Model AI Hardware Accelerators [77.89070422157178]
  Manufacturers of AI-integrated products face a critical challenge: selecting an accelerator that aligns with their product's performance requirements. The Abstract Computer Architecture Description Language (ACADL) is a concise formalization of computer architecture block diagrams. In this paper, we demonstrate how to use the ACADL to model AI hardware accelerators, use their ACADL description to map DNNs onto them, and explain the timing simulation semantics to gather performance results.
  arXiv  Detail & Related papers  (2024-01-30T19:27:16Z)
• Machine Learning Insides OptVerse AI Solver: Design Principles and Applications [74.67495900436728]
  We present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI solver. We showcase our methods for generating complex SAT and MILP instances utilizing generative models that mirror multifaceted structures of real-world problem. We detail the incorporation of state-of-the-art parameter tuning algorithms which markedly elevate solver performance.
  arXiv  Detail & Related papers  (2024-01-11T15:02:15Z)
• Edge AI Inference in Heterogeneous Constrained Computing: Feasibility and Opportunities [9.156192191794567]
  The proliferation of AI inference accelerators showcases innovation but also underscores challenges. This paper outlines the requirements and components of a framework that accommodates hardware diversity. Next, we assess the impact of device heterogeneity on AI inference performance, identifying strategies to optimize outcomes without compromising service quality.
  arXiv  Detail & Related papers  (2023-10-27T16:46:59Z)
• Enable Deep Learning on Mobile Devices: Methods, Systems, and Applications [46.97774949613859]
  Deep neural networks (DNNs) have achieved unprecedented success in the field of artificial intelligence (AI) However, their superior performance comes at the considerable cost of computational complexity. This paper provides an overview of efficient deep learning methods, systems and applications.
  arXiv  Detail & Related papers  (2022-04-25T16:52:48Z)
• Enabling Automated Machine Learning for Model-Driven AI Engineering [60.09869520679979]
  We propose a novel approach to enable Model-Driven Software Engineering and Model-Driven AI Engineering. In particular, we support Automated ML, thus assisting software engineers without deep AI knowledge in developing AI-intensive systems.
  arXiv  Detail & Related papers  (2022-03-06T10:12:56Z)
• Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approach [81.5261621619557]
  We propose an adaptive anomaly detection scheme with hierarchical edge computing (HEC) We first construct multiple anomaly detection DNN models with increasing complexity, and associate each of them to a corresponding HEC layer. Then, we design an adaptive model selection scheme that is formulated as a contextual-bandit problem and solved by using a reinforcement learning policy network.
  arXiv  Detail & Related papers  (2021-08-09T08:45:47Z)
• How to Reach Real-Time AI on Consumer Devices? Solutions for Programmable and Custom Architectures [7.085772863979686]
  Deep neural networks (DNNs) have led to large strides in various Artificial Intelligence (AI) inference tasks, such as object and speech recognition. deploying such AI models across commodity devices faces significant challenges. We present techniques for achieving real-time performance following a cross-stack approach.
  arXiv  Detail & Related papers  (2021-06-21T11:23:12Z)
• Towards AIOps in Edge Computing Environments [60.27785717687999]
  This paper describes the system design of an AIOps platform which is applicable in heterogeneous, distributed environments. It is feasible to collect metrics with a high frequency and simultaneously run specific anomaly detection algorithms directly on edge devices.
  arXiv  Detail & Related papers  (2021-02-12T09:33:00Z)
• Reconfigurable Intelligent Surface Assisted Mobile Edge Computing with Heterogeneous Learning Tasks [53.1636151439562]
  Mobile edge computing (MEC) provides a natural platform for AI applications. We present an infrastructure to perform machine learning tasks at an MEC with the assistance of a reconfigurable intelligent surface (RIS) Specifically, we minimize the learning error of all participating users by jointly optimizing transmit power of mobile users, beamforming vectors of the base station, and the phase-shift matrix of the RIS.
  arXiv  Detail & Related papers  (2020-12-25T07:08:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.