Weightless Neural Networks for Efficient Edge Inference

Susskind, Zachary; Arora, Aman; Miranda, Igor; Villon, Luis A. Q.; Katopodis, Rafael F.; Araújo, Leandro Santiago de; Dutra, Diego Leonel Cadette; Lima, Priscila M. V.; França, Felipe M. G.; Breternitz, Maurício; John, Lizy K.

doi:10.1145/3559009.3569680

Cited by 10 publications

(2 citation statements)

References 20 publications

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“…For each class in the problem domain, WiSARD stores a set of RAM nodes in the form of a discriminator that is responsible for recognizing patterns of that class. Besides lower training time, models like WiSARD present a structure that can be directly implemented at a hardware level, which makes them suitable for highperformance and low-power embedded applications 37, 38 .…”

Section: Methodsmentioning

confidence: 99%

A Data-Driven Approach for Estimating Temperature Variations Based on B-mode Ultrasound Images and Changes in Backscattered Energy

Oliveira,

França,

Pereira

2023

Ultrason Imaging

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Thermal treatments that use ultrasound devices as a tool have as a key point the temperature control to be applied in a specific region of the patient’s body. This kind of procedure requires caution because the wrong regulation can either limit the treatment or aggravate an existing injury. Therefore, determining the temperature in a region of interest in real-time is a subject of high interest. Although this is still an open problem, in the field of ultrasound analysis, the use of machine learning as a tool for both imaging and automated diagnostics are application trends. In this work, a data-driven approach is proposed to address the problem of estimating the temperature in regions of a B-mode ultrasound image as a supervised learning problem. The proposal consists in presenting a novel data modeling for the problem that includes information retrieved from conventional B-mode ultrasound images and a parametric image built based on changes in backscattered energy (CBE). Then, we compare the performance of classic models in the literature. The computational results presented that, in a simulated scenario, the proposed approach that a Gradient Boosting model would be able to estimate the temperature with a mean absolute error of around 0.5°C, which is acceptable in practical environments both in physiotherapic treatments and high intensity focused ultrasound (HIFU).

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Section: Methodsmentioning

confidence: 99%

A Data-Driven Approach for Estimating Temperature Variations Based on B-mode Ultrasound Images and Changes in Backscattered Energy

Oliveira,

França,

Pereira

2023

Ultrason Imaging

Self Cite

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“…(iv) N-Tuple classifers: Also known as n-tuple classifers, WNNs represent a distinctive classifcation methodology that enhances their efectiveness and fexibility [16]…”

Section: Introductionmentioning

confidence: 99%

Enhancing Tire Condition Monitoring through Weightless Neural Networks Using MEMS-Based Vibration Signals

Arora,

Naveen Venkatesh,

Sugumaran

et al. 2024

Journal of Engineering

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Tire pressure monitoring system (TPMS) has a critical role in safeguarding vehicle safety by monitoring tire pressure levels. Keeping the accurate tire pressure is necessary for confirming comfortable driving and safety, and improving fuel consumption. Tire problems can result from various factors, such as road surface conditions, weather changes, and driving activities, emphasizing the importance of systematic tire checks. This study presents a novel method for tire condition monitoring using weightless neural networks (WNN), which mimic neural processes using random-access memory (RAM) components, supporting fast and precise training. Wilkes, Stonham, and Aleksander Recognition Device (WiSARD), a type of WNN, stands out for its capability in classification and pattern recognition, gaining from its ability to avoid repetitive training and residual formation. For vibration data acquisition from tires, cost-effective micro-electro-mechanical system (MEMS) sensors are employed, offering a more economical solution than piezoelectric sensors. This approach yields a variety of features, such as autoregressive moving average (ARMA), statistical and histogram features. The J48 decision tree algorithm plays a critical role in selecting essential features for classification, which are subsequently divided into training and testing sets, crucial for assessing the WiSARD classifier’s efficacy. Hyperparameter optimization of the WNN leads to improved classification accuracy and shorter computation times. In practical tests, the WiSARD classifier, when optimally configured, achieved an impressive 97.92% accuracy with histogram features in only 0.008 seconds, showcasing the capability of WNN to enhance tire technology and the accuracy and efficiency of tire monitoring and maintenance.

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Object modeling through weightless tracking

do Nascimento,

França

2024

Neural Comput & Applic

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Weightless Neural Networks for Efficient Edge Inference

Cited by 10 publications

References 20 publications

A Data-Driven Approach for Estimating Temperature Variations Based on B-mode Ultrasound Images and Changes in Backscattered Energy

A Data-Driven Approach for Estimating Temperature Variations Based on B-mode Ultrasound Images and Changes in Backscattered Energy

Enhancing Tire Condition Monitoring through Weightless Neural Networks Using MEMS-Based Vibration Signals

Object modeling through weightless tracking

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