Embedded sensors, communication technologies, computing platforms and machine learning for UAVs: A review
… -art embedded sensors, communication technologies, computing platforms and machine
learning … This review will concentrate on some of the most recently developed machine learning …
learning … This review will concentrate on some of the most recently developed machine learning …
[HTML][HTML] An overview of machine learning within embedded and mobile devices–optimizations and applications
… this convergence of embedded systems and machine learning. … in embedded machine
learning and review different optimization techniques to enhance the execution of deep learning …
learning and review different optimization techniques to enhance the execution of deep learning …
ESP4ML: Platform-based design of systems-on-chip for embedded machine learning
… of specialized accelerators for machine learning (ML) has … for enabling machine learning
into embedded devices at the … However, to realize innovative embedded systems for such …
into embedded devices at the … However, to realize innovative embedded systems for such …
[HTML][HTML] A review of embedded machine learning based on hardware, application, and sensing scheme
… the implementation of embedded machine learning somewhat difficult. … of machine learning
on embedded systems divided by their specific device, application, specific machine learning …
on embedded systems divided by their specific device, application, specific machine learning …
Optimising resource management for embedded machine learning
… embedded platforms, at both design time and runtime. At design time, DNNs are deployed
on a variety of hardware platforms … and optimise machine learning workloads alongside other …
on a variety of hardware platforms … and optimise machine learning workloads alongside other …
[HTML][HTML] Design and evaluation of a new machine learning framework for IoT and embedded devices
G Cornetta, A Touhafi - Electronics, 2021 - mdpi.com
… and a plethora of new platforms are available on the market. Some of them either have
embedded GPUs or the possibility to be connected to external Machine Learning (ML) algorithm …
embedded GPUs or the possibility to be connected to external Machine Learning (ML) algorithm …
[PDF][PDF] Bringing machine learning to embedded systems
M Nadeski - Texas Instruments, 2019 - docs.ampnuts.ru
… system, the neural network will often be trained on a different processing platform than the
… the inference part of deep learning. The terms “deep learning” and “machine learning” in the …
… the inference part of deep learning. The terms “deep learning” and “machine learning” in the …
Implementation of machine learning algorithm in embedded devices
… The machine that is learning must be able to create a general … Machine learning algorithms
can be implemented in available microcontrollers, which are used in various embedded …
can be implemented in available microcontrollers, which are used in various embedded …
[HTML][HTML] The Embedded Machine Learning Revolution: The Basics You Need To Know
J Soldatos - wevolver.com
… Furthermore, to cope with machine learning in a variety of embedded platforms, developers
and deployers must be familiar with a variety of different systems and tools, which is yet …
and deployers must be familiar with a variety of different systems and tools, which is yet …
[HTML][HTML] Machine learning in resource-scarce embedded systems, FPGAs, and end-devices: A survey
… Building a machine learning model may take a few steps, but we are only going to focus
on the two most important ones: training/learning and validation/simulation phases. The …
on the two most important ones: training/learning and validation/simulation phases. The …
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