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Found 9 papers, 4 papers with code
Deep denoising autoencoder-based non-invasive blood flow detection for arteriovenous fistula
Clinical guidelines underscore the importance of regularly monitoring and surveilling arteriovenous fistula (AVF) access in hemodialysis patients to promptly detect any dysfunction.
DREAM: A Dynamic Scheduler for Dynamic Real-time Multi-model ML Workloads
Such dynamic behaviors introduce new challenges to the system software in an ML system since the overall system load is not completely predictable, unlike traditional ML workloads.
Multi-Scale High-Resolution Vision Transformer for Semantic Segmentation
Therefore, we propose HRViT, which enhances ViTs to learn semantically-rich and spatially-precise multi-scale representations by integrating high-resolution multi-branch architectures with ViTs.
Ranked #26 on
Semantic Segmentation
on Cityscapes val
Eyeriss v2: A Flexible Accelerator for Emerging Deep Neural Networks on Mobile Devices
In this work, we present Eyeriss v2, a DNN accelerator architecture designed for running compact and sparse DNNs.
Efficient Processing of Deep Neural Networks: A Tutorial and Survey
The reader will take away the following concepts from this article: understand the key design considerations for DNNs; be able to evaluate different DNN hardware implementations with benchmarks and comparison metrics; understand the trade-offs between various hardware architectures and platforms; be able to evaluate the utility of various DNN design techniques for efficient processing; and understand recent implementation trends and opportunities.
Towards Closing the Energy Gap Between HOG and CNN Features for Embedded Vision
Computer vision enables a wide range of applications in robotics/drones, self-driving cars, smart Internet of Things, and portable/wearable electronics.
Hardware for Machine Learning: Challenges and Opportunities
Machine learning plays a critical role in extracting meaningful information out of the zetabytes of sensor data collected every day.
Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning
With the proposed pruning method, the energy consumption of AlexNet and GoogLeNet are reduced by 3. 7x and 1. 6x, respectively, with less than 1% top-5 accuracy loss.
