Convergence of edge computing and deep learning: A comprehensive survey
Ubiquitous sensors and smart devices from factories and communities are generating
massive amounts of data, and ever-increasing computing power is driving the core of …
massive amounts of data, and ever-increasing computing power is driving the core of …
[HTML][HTML] Ai system engineering—key challenges and lessons learned
The main challenges are discussed together with the lessons learned from past and
ongoing research along the development cycle of machine learning systems. This will be …
ongoing research along the development cycle of machine learning systems. This will be …
A runtime-adaptive cognitive IoT node for healthcare monitoring
Wearable and energy efficient processing nodes, allowing for continuous remote monitoring
of patient vital parameters, are mainstream in modern health-care practice. Most recent …
of patient vital parameters, are mainstream in modern health-care practice. Most recent …
Hw-flowq: A multi-abstraction level hw-cnn co-design quantization methodology
Model compression through quantization is commonly applied to convolutional neural
networks (CNNs) deployed on compute and memory-constrained embedded platforms …
networks (CNNs) deployed on compute and memory-constrained embedded platforms …
[HTML][HTML] An automated design flow for adaptive neural network hardware accelerators
Image and video processing are one of the main driving application fields for the latest
technology advancement of computing platforms, especially considering the adoption of …
technology advancement of computing platforms, especially considering the adoption of …
Applying AI in practice: key challenges and lessons learned
The main challenges along with lessons learned from ongoing research in the application of
machine learning systems in practice are discussed, taking into account aspects of …
machine learning systems in practice are discussed, taking into account aspects of …
[PDF][PDF] Ease: Energy optimization through adaptation–a review of runtime energy-aware approximate deep learning algorithms
EASE: Energy Optimization through Adaptation – A Review of Runtime Energy-Aware
Approximate Deep Learning Algorithms Page 1 P osted on 6 F eb 2024 — CC-BY 4.0 — h …
Approximate Deep Learning Algorithms Page 1 P osted on 6 F eb 2024 — CC-BY 4.0 — h …
[HTML][HTML] Designing convolutional neural networks with constrained evolutionary piecemeal training
D Sapra, AD Pimentel - Applied Intelligence, 2022 - Springer
The automated architecture search methodology for neural networks is known as Neural
Architecture Search (NAS). In recent times, Convolutional Neural Networks (CNNs) …
Architecture Search (NAS). In recent times, Convolutional Neural Networks (CNNs) …
Edge computing for artificial intelligence
Extensive deployment of AI services, especially mobile AI, requires the support of edge
computing. This support is not just at the network architecture level, the design, adaptation …
computing. This support is not just at the network architecture level, the design, adaptation …
[HTML][HTML] Runtime Adaptive IoMT Node on Multi-Core Processor Platform
The Internet of Medical Things (IoMT) paradigm is becoming mainstream in multiple clinical
trials and healthcare procedures. Thanks to innovative technologies, latest-generation …
trials and healthcare procedures. Thanks to innovative technologies, latest-generation …