Cellular, wide-area, and non-terrestrial IoT: A survey on 5G advances and the road toward 6G
The next wave of wireless technologies is proliferating in connecting things among
themselves as well as to humans. In the era of the Internet of Things (IoT), billions of …
themselves as well as to humans. In the era of the Internet of Things (IoT), billions of …
[HTML][HTML] A review on deep learning in UAV remote sensing
Abstract Deep Neural Networks (DNNs) learn representation from data with an impressive
capability, and brought important breakthroughs for processing images, time-series, natural …
capability, and brought important breakthroughs for processing images, time-series, natural …
AWQ: Activation-aware Weight Quantization for On-Device LLM Compression and Acceleration
Large language models (LLMs) have shown excellent performance on various tasks, but the
astronomical model size raises the hardware barrier for serving (memory size) and slows …
astronomical model size raises the hardware barrier for serving (memory size) and slows …
Smoothquant: Accurate and efficient post-training quantization for large language models
Large language models (LLMs) show excellent performance but are compute-and memory-
intensive. Quantization can reduce memory and accelerate inference. However, existing …
intensive. Quantization can reduce memory and accelerate inference. However, existing …
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
CY Wang, A Bochkovskiy… - Proceedings of the IEEE …, 2023 - openaccess.thecvf.com
Real-time object detection is one of the most important research topics in computer vision.
As new approaches regarding architecture optimization and training optimization are …
As new approaches regarding architecture optimization and training optimization are …
On-device training under 256kb memory
On-device training enables the model to adapt to new data collected from the sensors by
fine-tuning a pre-trained model. Users can benefit from customized AI models without having …
fine-tuning a pre-trained model. Users can benefit from customized AI models without having …
Tensorflow lite micro: Embedded machine learning for tinyml systems
R David, J Duke, A Jain… - Proceedings of …, 2021 - proceedings.mlsys.org
Abstract We introduce TensorFlow (TF) Micro, an open-source machine learning inference
framework for running deep-learning models on embedded systems. TF Micro tackles the …
framework for running deep-learning models on embedded systems. TF Micro tackles the …
Memory-efficient patch-based inference for tiny deep learning
Tiny deep learning on microcontroller units (MCUs) is challenging due to the limited memory
size. We find that the memory bottleneck is due to the imbalanced memory distribution in …
size. We find that the memory bottleneck is due to the imbalanced memory distribution in …
Machine learning for microcontroller-class hardware: A review
The advancements in machine learning (ML) opened a new opportunity to bring intelligence
to the low-end Internet-of-Things (IoT) nodes, such as microcontrollers. Conventional ML …
to the low-end Internet-of-Things (IoT) nodes, such as microcontrollers. Conventional ML …
Micronets: Neural network architectures for deploying tinyml applications on commodity microcontrollers
Executing machine learning workloads locally on resource constrained microcontrollers
(MCUs) promises to drastically expand the application space of IoT. However, so-called …
(MCUs) promises to drastically expand the application space of IoT. However, so-called …