2,370 research outputs found
Generative Model for Models: Rapid DNN Customization for Diverse Tasks and Resource Constraints
Unlike cloud-based deep learning models that are often large and uniform,
edge-deployed models usually demand customization for domain-specific tasks and
resource-limited environments. Such customization processes can be costly and
time-consuming due to the diversity of edge scenarios and the training load for
each scenario. Although various approaches have been proposed for rapid
resource-oriented customization and task-oriented customization respectively,
achieving both of them at the same time is challenging. Drawing inspiration
from the generative AI and the modular composability of neural networks, we
introduce NN-Factory, an one-for-all framework to generate customized
lightweight models for diverse edge scenarios. The key idea is to use a
generative model to directly produce the customized models, instead of training
them. The main components of NN-Factory include a modular supernet with
pretrained modules that can be conditionally activated to accomplish different
tasks and a generative module assembler that manipulate the modules according
to task and sparsity requirements. Given an edge scenario, NN-Factory can
efficiently customize a compact model specialized in the edge task while
satisfying the edge resource constraints by searching for the optimal strategy
to assemble the modules. Based on experiments on image classification and
object detection tasks with different edge devices, NN-Factory is able to
generate high-quality task- and resource-specific models within few seconds,
faster than conventional model customization approaches by orders of magnitude
Auto-CsiNet: Scenario-customized Automatic Neural Network Architecture Generation for Massive MIMO CSI Feedback
Deep learning has revolutionized the design of the channel state information
(CSI) feedback module in wireless communications. However, designing the
optimal neural network (NN) architecture for CSI feedback can be a laborious
and time-consuming process. Manual design can be prohibitively expensive for
customizing NNs to different scenarios. This paper proposes using neural
architecture search (NAS) to automate the generation of scenario-customized CSI
feedback NN architectures, thereby maximizing the potential of deep learning in
exclusive environments. By employing automated machine learning and
gradient-descent-based NAS, an efficient and cost-effective architecture design
process is achieved. The proposed approach leverages implicit scene knowledge,
integrating it into the scenario customization process in a data-driven manner,
and fully exploits the potential of deep learning for each specific scenario.
To address the issue of excessive search, early stopping and elastic selection
mechanisms are employed, enhancing the efficiency of the proposed scheme. The
experimental results demonstrate that the automatically generated architecture,
known as Auto-CsiNet, outperforms manually-designed models in both
reconstruction performance (achieving approximately a 14% improvement) and
complexity (reducing it by approximately 50%). Furthermore, the paper analyzes
the impact of the scenario on the NN architecture and its capacity.Comment: 16 pages, 10 figures, 6 table
Machine Learning in Manufacturing towards Industry 4.0: From ‘For Now’ to ‘Four-Know’
While attracting increasing research attention in science and technology, Machine Learning (ML) is playing a critical role in the digitalization of manufacturing operations towards Industry 4.0. Recently, ML has been applied in several fields of production engineering to solve a variety of tasks with different levels of complexity and performance. However, in spite of the enormous number of ML use cases, there is no guidance or standard for developing ML solutions from ideation to deployment. This paper aims to address this problem by proposing an ML application roadmap for the manufacturing industry based on the state-of-the-art published research on the topic. First, this paper presents two dimensions for formulating ML tasks, namely, ’Four-Know’ (Know-what, Know-why, Know-when, Know-how) and ’Four-Level’ (Product, Process, Machine, System). These are used to analyze ML development trends in manufacturing. Then, the paper provides an implementation pipeline starting from the very early stages of ML solution development and summarizes the available ML methods, including supervised learning methods, semi-supervised methods, unsupervised methods, and reinforcement methods, along with their typical applications. Finally, the paper discusses the current challenges during ML applications and provides an outline of possible directions for future developments
ECG-Based Arrhythmia Classification using Recurrent Neural Networks in Embedded Systems
Cardiac arrhythmia is one of the most important cardiovascular diseases (CVDs), causing million deaths every year. Moreover it is difficult to diagnose because it occurs intermittently and as such requires the analysis of large amount of data, collected during the daily life of patients. An important tool for CVD diagnosis is the analysis of electrocardiogram (ECG), because of its non-invasive nature and simplicity of acquisition. In this work we propose a classification algorithm for arrhythmia based on recurrent neural networks (RNNs) that operate directly on ECG data, exploring the effectiveness and efficiency of several variations of the general RNN, in particular using different types of layers implementing the network memory. We use the MIT-BIH arrhythmia database and the evaluation protocol recommended by the Association for the Advancement of Medical Instrumentation (AAMI). After designing and testing the effectiveness of the different networks, we then test its porting to an embedded platform, namely the STM32 microcontroller architecture from ST, using a specific framework to port a pre-built RNN to the embedded hardware, convert it to optimized code for the platform and evaluate its performance in terms of resource usage. Both in binary and multiclass classification, the basic RNN model outperforms the other architectures in terms of memory storage (∼117 KB), number of parameters (∼5 k) and inference time (∼150 ms), while the RNN LSTM-based achieved the best accuracy (∼90%)
An Efficient General-Purpose Modular Vision Model via Multi-Task Heterogeneous Training
We present a model that can perform multiple vision tasks and can be adapted
to other downstream tasks efficiently. Despite considerable progress in
multi-task learning, most efforts focus on learning from multi-label data: a
single image set with multiple task labels. Such multi-label data sets are
rare, small, and expensive. We say heterogeneous to refer to image sets with
different task labels, or to combinations of single-task datasets. Few have
explored training on such heterogeneous datasets. General-purpose vision models
are still dominated by single-task pretraining, and it remains unclear how to
scale up multi-task models by leveraging mainstream vision datasets designed
for different purposes. The challenges lie in managing large intrinsic
differences among vision tasks, including data distribution, architectures,
task-specific modules, dataset scales, and sampling strategies. To address
these challenges, we propose to modify and scale up mixture-of-experts (MoE)
vision transformers, so that they can simultaneously learn classification,
detection, and segmentation on diverse mainstream vision datasets including
ImageNet, COCO, and ADE20K. Our approach achieves comparable results to
single-task state-of-the-art models and demonstrates strong generalization on
downstream tasks. Due to its emergent modularity, this general-purpose model
decomposes into high-performing components, efficiently adapting to downstream
tasks. We can fine-tune it with fewer training parameters, fewer model
parameters, and less computation. Additionally, its modularity allows for easy
expansion in continual-learning-without-forgetting scenarios. Finally, these
functions can be controlled and combined to meet various demands of downstream
tasks
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