Integration of Convolutional Neural Networks in Mobile Applications

• URL: http://arxiv.org/abs/2103.07286v1
• Date: Thu, 11 Mar 2021 15:27:05 GMT
• Title: Integration of Convolutional Neural Networks in Mobile Applications
• Authors: Roger Creus Castanyer and Silverio Mart\'inez-Fern\'andez and Xavier Franch
• Abstract summary: We study the performance of a system that integrates a Deep Learning model as a trade-off between the accuracy and the complexity. We identify the most concerning challenges when deploying DL-based software in mobile applications.
• Score: 3.0280987248827085
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: When building Deep Learning (DL) models, data scientists and software engineers manage the trade-off between their accuracy, or any other suitable success criteria, and their complexity. In an environment with high computational power, a common practice is making the models go deeper by designing more sophisticated architectures. However, in the context of mobile devices, which possess less computational power, keeping complexity under control is a must. In this paper, we study the performance of a system that integrates a DL model as a trade-off between the accuracy and the complexity. At the same time, we relate the complexity to the efficiency of the system. With this, we present a practical study that aims to explore the challenges met when optimizing the performance of DL models becomes a requirement. Concretely, we aim to identify: (i) the most concerning challenges when deploying DL-based software in mobile applications; and (ii) the path for optimizing the performance trade-off. We obtain results that verify many of the identified challenges in the related work such as the availability of frameworks and the software-data dependency. We provide a documentation of our experience when facing the identified challenges together with the discussion of possible solutions to them. Additionally, we implement a solution to the sustainability of the DL models when deployed in order to reduce the severity of other identified challenges. Moreover, we relate the performance trade-off to a new defined challenge featuring the impact of the complexity in the obtained accuracy. Finally, we discuss and motivate future work that aims to provide solutions to the more open challenges found.

Related papers

• Generative AI Agents with Large Language Model for Satellite Networks via a Mixture of Experts Transmission [74.10928850232717]
  This paper develops generative artificial intelligence (AI) agents for model formulation and then applies a mixture of experts (MoE) to design transmission strategies. Specifically, we leverage large language models (LLMs) to build an interactive modeling paradigm. We propose an MoE-proximal policy optimization (PPO) approach to solve the formulated problem.
  arXiv  Detail & Related papers  (2024-04-14T03:44:54Z)
• Characterization of Large Language Model Development in the Datacenter [55.9909258342639]
  Large Language Models (LLMs) have presented impressive performance across several transformative tasks. However, it is non-trivial to efficiently utilize large-scale cluster resources to develop LLMs. We present an in-depth characterization study of a six-month LLM development workload trace collected from our GPU datacenter Acme.
  arXiv  Detail & Related papers  (2024-03-12T13:31:14Z)
• Improving QA Model Performance with Cartographic Inoculation [0.0]
  "Dataset artifacts" reduce the model's ability to generalize to real-world QA problems. We analyze the impacts and incidence of dataset artifacts using an adversarial challenge set. We show that by selectively fine-tuning a model on ambiguous adversarial examples from a challenge set, significant performance improvements can be made.
  arXiv  Detail & Related papers  (2024-01-30T23:08:26Z)
• Enabling Resource-efficient AIoT System with Cross-level Optimization: A survey [20.360136850102833]
  This survey aims to provide a broader optimization space for more free resource-performance tradeoffs. By consolidating problems and techniques scattered over diverse levels, we aim to help readers understand their connections and stimulate further discussions.
  arXiv  Detail & Related papers  (2023-09-27T08:04:24Z)
• Deep Transfer Learning for Automatic Speech Recognition: Towards Better Generalization [3.6393183544320236]
  Speech recognition has become an important challenge when using deep learning (DL) It requires large-scale training datasets and high computational and storage resources. Deep transfer learning (DTL) has been introduced to overcome these issues.
  arXiv  Detail & Related papers  (2023-04-27T21:08:05Z)
• Exposing and Addressing Cross-Task Inconsistency in Unified Vision-Language Models [80.23791222509644]
  Inconsistent AI models are considered brittle and untrustworthy by human users. We find that state-of-the-art vision-language models suffer from a surprisingly high degree of inconsistent behavior across tasks. We propose a rank correlation-based auxiliary training objective, computed over large automatically created cross-task contrast sets.
  arXiv  Detail & Related papers  (2023-03-28T16:57:12Z)
• GLUECons: A Generic Benchmark for Learning Under Constraints [102.78051169725455]
  In this work, we create a benchmark that is a collection of nine tasks in the domains of natural language processing and computer vision. We model external knowledge as constraints, specify the sources of the constraints for each task, and implement various models that use these constraints.
  arXiv  Detail & Related papers  (2023-02-16T16:45:36Z)
• Matching DNN Compression and Cooperative Training with Resources and Data Availability [20.329698347331075]
  How much and when an ML model should be compressed, and em where its training should be executed, are hard decisions to make. We model the network system focusing on the training of DNNs, formalize the multi-dimensional problem, and formulate an approximate dynamic programming problem. We prove that PACT's solutions can get as close to the optimum as desired, at the cost of an increased time complexity.
  arXiv  Detail & Related papers  (2022-12-02T09:52:18Z)
• Which Design Decisions in AI-enabled Mobile Applications Contribute to Greener AI? [7.194465440864905]
  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.
  arXiv  Detail & Related papers  (2021-09-28T07:30:28Z)
• Interpretable Hyperspectral AI: When Non-Convex Modeling meets Hyperspectral Remote Sensing [57.52865154829273]
  Hyperspectral imaging, also known as image spectrometry, is a landmark technique in geoscience remote sensing (RS) In the past decade efforts have been made to process analyze these hyperspectral (HS) products mainly by means of seasoned experts. For this reason, it is urgent to develop more intelligent and automatic approaches for various HS RS applications.
  arXiv  Detail & Related papers  (2021-03-02T03:32:10Z)
• Goal-Aware Prediction: Learning to Model What Matters [105.43098326577434]
  One of the fundamental challenges in using a learned forward dynamics model is the mismatch between the objective of the learned model and that of the downstream planner or policy. We propose to direct prediction towards task relevant information, enabling the model to be aware of the current task and encouraging it to only model relevant quantities of the state space. We find that our method more effectively models the relevant parts of the scene conditioned on the goal, and as a result outperforms standard task-agnostic dynamics models and model-free reinforcement learning.
  arXiv  Detail & Related papers  (2020-07-14T16:42:59Z)

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.