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SRAM optimized porting and execution of machine learning classifiers on MCU-based IoT devices: Demo abstract
(Association for Computing Machinery (ACM), 2021-05-19)
With the introduction of edge analytics, IoT devices are becoming smarter and ready for AI applications. However, any increase in the training data results in a linear increase in the space complexity of the trained Machine ...
Edge2Guard: Botnet attacks detecting offline models for resource-constrained IoT devices
(National University of Ireland Galway, 2021-03-22)
In today's IoT smart environments, dozens of MCU-based connected device types exist such as HVAC controllers, smart meters, smoke detectors, etc. The security conditions for these essential IoT devices remain unsatisfactory ...
RCE-NN: a five-stage pipeline to execute neural networks (CNNs) on resource-constrained IoT edge devices
(Association for Computing Machinery (ACM), 2020-10-06)
Microcontroller Units (MCUs) in edge devices are resource constrained due to their limited memory footprint, fewer computation cores, and low clock speeds. These limitations constrain one from deploying and executing machine ...
Edge2Train: A framework to train machine learning models (SVMs) on resource-constrained IoT edge devices
(Association for Computing Machinery (ACM), 2020-10-06)
In recent years, ML (Machine Learning) models that have been trained in data centers can often be deployed for use on edge devices. When the model deployed on these devices encounters unseen data patterns, it will either ...
Enabling machine learning on the edge using SRAM conserving efficient neural networks execution approach
(National University of Ireland Galway, 2021-09-13)
Edge analytics refers to the application of data analytics and
Machine Learning (ML) algorithms on IoT devices. The concept of edge
analytics is gaining popularity due to its ability to perform AI-based analytics at the ...
Toward distributed, global, deep learning using IoT devices
(Institute of Electrical and Electronics Engineers (IEEE), 2021-07-20)
Deep learning (DL) using large scale, high-quality IoT datasets can be computationally expensive. Utilizing such datasets to produce a problem-solving model within a reasonable time frame requires a scalable distributed ...
Ultra-fast machine learning classifier execution on IoT devices without SRAM consumption
(Institute of Electrical and Electronics Engineers (IEEE), 2021-05-25)
With the introduction of edge analytics, IoT devices are becoming smart and ready for AI applications. A few modern ML frameworks are focusing on the generation of small-size ML models (often in kBs) that can directly be ...
Porting and execution of anomalies detection models on embedded systems in IoT: Demo abstract
(Association for Computing Machinery (ACM), 2021-05-18)
In the Industry 4.0 era, Microcontrollers (MCUs) based tiny embedded sensor systems have become the sensing paradigm to interact with the physical world. In 2020, 25.6 billion MCUs were
shipped, and over 250 billion MCUs ...
An SRAM optimized approach for constant memory consumption and ultra-fast execution of ML classifiers on TinyML hardware
(Institute of Electrical and Electronics Engineers, 2021-11-15)
With the introduction of ultra-low-power machine
learning (TinyML), IoT devices are becoming smarter as they are
driven by Machine Learning (ML) models. However, any increase
in the training data results in a linear ...