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Showing 1 to 20 of 174 for “"DNN"”.
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Congestion Control for DNN training clusters
The modern DNN workloads generate network traffic having striking differences with the conventional data-center traffic. DNN training jobs generate periodic traffic pattern where all subsequent flows depend on the completion of the currently running flow. Although this periodic behavior calls for a …
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An adaptive pruning algorithm for DNN compression
In recent years, deep neural networks have achieved remarkable results in various artificial intelligence tasks such as image recognition and machine language translation. Although deep neural networks achieve state-of-the-art accuracy for various tasks, the high accuracy comes with high …
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ACCELERATING DNN INFERENCE AND TRAINING IN DISTRIBUTED SYSTEMS
Deep Neural Network (DNN) models have been widely deployed in a variety of applications. To achieve better performance, DNN models become more and more complex, which introduces extremely long DNN training time. Although DNN inference typically runs a single round of forward propagation on the DNN …
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Energy-aware DNN Quantization for Processing-In-Memory Architecture
… computational cost of deep neural network (DNN), many efforts to develop energy-efficient intelligent system have been proposed from dedicated hardware platforms to model compression algorithms. Recently, hardware-aware quantization algorithms have shown further improvement in the energy …
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Surface electromyography signal classification using SFDN+DNN for hand gesture recognition
… As a result, the novel method is called SFDN+DNN. There are many complex and developed prostheses in the market. However, the bottleneck to improve the capabilities of the prostheses to a quasi-real-hand level is still a big challenge. In this thesis a thorough literature review of …
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Reducing Global Memory Accesses in DNN Training using Structured Weight Masking
Training large deep neural networks (DNNs) is often constrained by memory bandwidth, with frequent global memory accesses representing a significant performance bottleneck. This thesis investigates the potential of dynamic structured weight masking to alleviate this bottleneck during training, …
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Converting Autoencoder Based Energy-Efficient and Secure DNN Inference on Edge Devices
Deep neural networks (DNNs) have become essential for computer vision tasks like image classification, object detection, and depth estimation. With the rise of embedded devices, there is a growing demand for lightweight and energy-efficient models. While DNNs outperform traditional machine learning …
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Investigating Opportunities and Challenges in Modeling and Designing Scale-Out DNN Accelerators
… who would like to integrate an existing DNN accelerator architecture into a larger SoC and would be interested in system-level characterization results. The second use-case is for an accelerator architect who would like to use the tool to explore the accelerator design space by sweeping …
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GraphPipe: Improving the Performance and Scalability of DNN Training with Graph Pipeline Parallelism
Deep neural networks (DNNs) continue to grow rapidly in size, thus it is infeasible to train them on a single device. To address this challenge, current DNN training systems apply pipeline-parallel techniques. They split a DNN into multiple stages, construct a pipeline of them, and assign to each …
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Synthetic Data Generation and Sampling for Online Training of DNN in Manufacturing Supervised Learning Problems
… statistical models such as Deep Neural Networks (DNNs). Deep Neural Networks (DNNs) have proven to be remarkably effective in supervised learning in critical manufacturing applications, such as AI-enabled automatic inspection, quality modeling, etc. However, there is a lack of performance …
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Effects of Hardware Design Choices on Neural Network Accuracy in Analog Inference Accelerators
… and high-throughput deep neural network (DNN) computations by computing in memory. Unfortunately, device and circuit nonidealities in these accelerators, such as noise and quantization, can also lead to low DNN inference accuracy due to computation errors arising from these non-idealities. …
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Software-Hardware Co-design For Deep Learning Model Acceleration
<p>Current deep neural network (DNN) models have shown beyond-human performance in multiple artificial intelligent tasks. However, state-of-the-art DNN models still exhibit great issues on efficiency that pose significant obstacles to their practical application in real-world scenarios. To further …
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Dynamic Neural Network-based Adaptive Inverse Optimal Control Design
… introduces a Dynamical Neural Network (DNN) model based adaptive inverse optimal control design for a class of nonlinear systems. A DNN structure is developed and stabilized based on a control Lyapunov function (CLF). The CLF must satisfy the partial Hamilton Jacobi-Bellman (HJB) …
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Efficient, Accurate, and Flexible PIM Inference through Adaptable Low-Resolution Arithmetic
… to efficiently run Deep Neural Network (DNN) inference by reducing costly data movement and by using resistive RAM (ReRAM) for efficient analog compute. Unfortunately, overall PIM accelerator efficiency and throughput are limited by area/energy-intensive analog-to-digital converters …
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TwinDNN: A tale of two deep neural networks
… technologies for deep neural networks (DNNs), such as weight quantization, have been widely investigated to reduce the model size so that they can be implemented on hardware with strict resource restrictions. However, one major disadvantage of model compression is accuracy degradation. …
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Towards Secure Machine Learning Acceleration: Threats and Defenses Across Algorithms, Architecture, and Circuits
As deep neural networks (DNNs) are widely adopted for high-stakes applications that process sensitive private data and make critical decisions, security concerns about user data and DNN models are growing. In particular, hardware-level vulnerabilities can be exploited to undermine the …
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Coordination déglutitions non nutritives-respiration lors d'un stress postnatal chez l'agneau nouveau-né : effets de l'hypoxie/hypercapnie et de la fumée secondaire
… (SMSN). Ainsi, les déglutitions non-nutritives (DNN) se présentent comme une fonction fondamentale en période néonatale notamment pour la clairance des voies aériennes supérieures des sécrétions salivaires et des reflux laryngopharyngés. Il apparaît plus précisément que la coordination entre DNN …
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De-noising and de-blurring of images using deep neural networks
Deep Neural Networks (DNNs) [1] are often used for image reconstruction, but perform better reconstructing the low frequencies of the image than the high frequencies. This is especially the case when using noisy images. In this paper, we test using a Learning Synthesis Deep Neural Network (LS-DNN) …
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A sensory system for robots using evolutionary artificial neural networks.
… system, called the Distributed Neural Network (DNN) was based on the sensory-motor connections in the common toad, Bufo Bufo. The sparsely connected network architecture has features of modularity enhanced by the presence of lateral inhibitory connections. It was implemented using Evolutionary …
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Investigation in modeling a load-sensing pump using dynamic neural unit based dynamic neural networks
… neural unit (DNU) based dynamic neural network (DNN) in modeling a hydraulic component (specifically a load-sensing pump), and the model could be used in a simulation with any other required component model to aid in hydraulic system design. To be truly representative of the component, the neural …
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