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TinyML benchmark: Executing fully connected neural networks on commodity microcontrollers
(National University of Ireland Galway, 2021-06-20)
Recent advancements in the field of ultra-low-power
machine learning (TinyML) promises to unlock an entirely new
class of edge applications. However, continued progress is restrained by the lack of benchmarking Machine ...
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 ...
End-to-end tracing and congestion in a blockchain: A supply chain use case in hyperledger fabric
(IGI Global, 2021)
Modern supply chain applications are complex systems that play an important role in many different sectors. Supply chain management systems are implemented to handle increasing complexity and flows of goods. However, most ...
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 ...
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 ...