TLDR: EmbeddedML is a new machine learning library designed for speed and efficiency, particularly on embedded systems with limited resources. By rewriting and optimizing common ML algorithms (regression, classification, clustering, PCA) using NumPy, it achieves significantly faster training times compared to scikit-learn, often with comparable accuracy. Key optimizations include momentum-based weight updates, Batch Gradient Descent, and early stopping mechanisms. This makes advanced AI applications more accessible and portable for devices like Raspberry Pi and NVIDIA Jetson.
A new machine learning library called EmbeddedML has been developed to address the challenges of slow training times and high computational demands often associated with traditional machine learning libraries, especially when dealing with large and complex datasets. This innovative library aims to make advanced artificial intelligence more accessible and efficient, particularly for devices with limited resources, such as embedded systems.
The creators of EmbeddedML, H. H. Çalışkan and T. Koruk, recognized that popular libraries like scikit-learn and TensorFlow can struggle with performance on standard CPUs when datasets grow in size and complexity. To overcome this, they built EmbeddedML from the ground up, optimizing algorithms using statistical methods and leveraging the power of NumPy for accelerated mathematical operations. This includes crucial calculations like matrix multiplication, transposing, inverting matrices, calculating covariance, and finding eigenvalues and eigenvectors.
EmbeddedML is designed to integrate seamlessly with other widely used data science libraries such as Pandas, NumPy, and Matplotlib. It offers a comprehensive suite of machine learning algorithms, covering various tasks including:
Regression Algorithms
The library includes Simple Linear Regression, Multiple Linear Regression, and Polynomial Regression. These algorithms have been mathematically rewritten using NumPy to significantly reduce training time. For instance, in Multiple Linear Regression, EmbeddedML demonstrated an average of 4 times faster training compared to scikit-learn, with virtually no loss in accuracy.
Classification Algorithms
EmbeddedML features optimized versions of Logistic Regression, Support Vector Machines (SVM), K-Nearest Neighbor (KNN), and Naive Bayes. These have been modified and rewritten with NumPy to enhance both training speed and accuracy.
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Logistic Regression: EmbeddedML’s implementation uses the Adam optimizer and Batch Gradient Descent (BGD) instead of Stochastic Gradient Descent (SGD). This approach processes the entire dataset in batches, leading to more stable learning and an average of 4 times faster training on large datasets compared to scikit-learn.
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Support Vector Machines (SVM): The SVM algorithm in EmbeddedML is remarkably faster, achieving approximately 2 times speed-up on small datasets and up to 800 times speed-up on large datasets compared to scikit-learn. This is due to its NumPy implementation, momentum-based weight updates for stable learning, and an early termination mechanism that stops training once a certain accuracy threshold is met.
Clustering and Dimensionality Reduction
The library also incorporates the K-Means clustering algorithm for grouping data points and Principal Component Analysis (PCA) for dimensionality reduction. PCA, which involves data centralization, covariance matrix calculation, and eigenvalue/eigenvector finding, benefits greatly from NumPy’s speed for efficient processing of large datasets.
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Preprocessing and Metrics
EmbeddedML supports essential preprocessing techniques like Min-Max Scaler, Standard Scaler, and Train-Val Split to prepare data for model training. For evaluating model performance, it provides a range of metrics: R² Score, Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) for regression models; and Accuracy, Precision, Recall, and F1-Score, along with a confusion matrix, for classification models.
A significant advantage of EmbeddedML is its lightweight and optimized nature, making it installable via pip. This allows machine learning model training, traditionally requiring high-powered desktop computers, to be performed efficiently on low-resource embedded systems such as the Raspberry Pi, NVIDIA Jetson Orin Nano, and Orange Pi. This capability greatly enhances the portability and accessibility of AI applications, enabling Python-based AI solutions on embedded devices where C-based libraries might be harder to integrate with other Python data science tools.
The research paper details the mathematical underpinnings and performance comparisons of EmbeddedML against scikit-learn across various datasets. The results consistently show that EmbeddedML delivers substantial reductions in training times without compromising model accuracy, especially as dataset size and complexity increase. This makes it a compelling choice for real-time and resource-constrained applications.
The authors plan to expand EmbeddedML in the future to support more complex neural network architectures, including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM) networks. For more technical details, you can refer to the full research paper: EmbeddedML: A New Optimized and Fast Machine Learning Library.
