Glossary

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A

• ADC (Analog-to-Digital Converter): Converts an analog signal into digital.
• Algorithm: A procedure used for solving a problem or performing a computation.
• ARM Processor: CPUs based on the RISC architecture, used in SBCs.
• Artificial Intelligence (AI): The simulation of human intelligence in machines.
• Attention Mechanism: A technique used in neural networks to focus on specific parts of the input sequence when making predictions. It is crucial in the architecture of transformers, which are used in LLMs.

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B

• Bioinformatics: Computational technology in molecular biology.
• Biometric Monitoring: Tracking physiological data for health purposes.
• Bidirectional Encoder Representations from Transformers (BERT): A pre-trained language model that uses transformers to process text in both directions, improving understanding and context.

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C

• Classification: The task of determining what category (or class) an input belongs to.
• Connectivity: Methods and technologies for connecting IoT devices.
• Condition Monitoring: Tracking machine or component health.
• Cross-Compilation: Compiling code for an embedded system with a different architecture.

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D

• Data pipeline: In Edge Impulse a data pipeline can be part of a project or be stand-alone.
• Data Preprocessing: Cleaning and organizing raw data before model training.
• Deep Learning: ML subset using neural networks with many layers.
• Digital Twin: A virtual model of a physical process or product.

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E

• Edge Computing: Processing data near its generation point.
• Edge Impulse Studio: Development platform for AI on edge devices.
• Embedded Linux: Linux OS/kernel used in embedded systems.
• Embedded Programming: Writing software for embedded systems.
• Embedded System: Computer hardware and software for specific functions.
• Embedding: A representation of text where words or phrases are mapped to vectors of real numbers. Embeddings capture semantic meaning and are used as input to language models.
• EON Compiler: A compiler that optimizes neural network models for edge devices, reducing memory and computational requirements. EON: Edge Optimized Neural.
• Ethernet Port: Networking port on some SBCs.

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F

• Firmware: Software programmed into the read-only memory of electronic devices.
• Fine-tuning: The process of further training a pre-trained model on a specific task with labeled data. Fine-tuning helps the model adapt to particular tasks or domains.
• Float32: A type of numerical precision where each number is stored with decimal values and take 32 bits of computer memory to store.
• FOMO (Faster Objects More Objects): is a novel machine learning algorithm designed to bring object detection to highly constrained devices. It allows for real-time object detection, tracking, and counting on microcontrollers, using up to 30x less processing power and memory than MobileNet SSD or YOLOv5. FOMO is capable of running in less than 200K of RAM and is fully compatible with any MobileNetV2 model. Outputs centroids and can run on microcontrollers Learn more.
• FOMO-AD (Faster Objects More Objects for Anomaly Detection): is a learning block provided by Edge Impulse for handling defect identification in computer vision applications. It is designed for fast object detection deployment on resource-constrained devices like microcontrollers. The FOMO-AD learning block uses a combination of neural networks and Gaussian Mixture Models (GMMs) to identify unusual patterns or anomalies in image data that do not conform to expected behavior. Outputs a segmentation with anomaly scores. Learn more.

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G

• GPIO (General-Purpose Input/Output): Pins on an MCU controlled by the user.
• GPIO Header: Group of pins on an SBC for interfacing with other circuits.

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H

• Heat Sink: Component for dissipating heat in SBCs.

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I

• Impulse: An on-device optimized processing pipeline composed of a combination of preprocessing, DSP and ML models.
• Inference: Making predictions using a trained ML model.
• Inference Performance Metrics: Metrics to evaluate the performance of a machine learning model during inference. Learn more.
• Ingestion Service: Collecting and transferring data to Edge Impulse.
• Int8: A type of numerical precision where each number is stored as a whole number and take 8 bits of computer memory to store.
• Industrial Automation: Control systems for industrial process management.
• Interrupt: Signal indicating a need for immediate attention.
• IoT Device: A device connected to the Internet with computing capabilities.

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K

• Keras: A tool within TensorFlow that makes it easy to create and train deep learning models.

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L

• Label: A special type of metadata that is used during training to instruct the model on some property of the data.
• Linux OS: Operating system for many SBCs, known for its open-source nature.

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M

• Machine Learning (ML): AI field enabling systems to learn and improve from experience.
• MCU (Microcontroller Unit): A compact integrated circuit for specific operations.
• Medical Imaging: Visual representations of the interior of a body.
• Metadata: Additional information that is associated with a given data sample in the dataset.
• Microcontroller (MCU): A small computer on a single integrated circuit, often used in IoT devices.
• Model: A combination of algorithm and state that is trained to perform a particular task.
• Model Compression: Reducing machine learning model size and complexity.
• Multi-Impulse: Running multiple machine learning models simultaneously on the same device. Learn more.

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N

• Neural network: A model whose structure is inspired by networks of biological neurons.
• Neural Processing Unit (NPU): Specialized hardware for efficient neural network computations.

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O

• Object detection: The task of identifying and localizing specific objects within an image.
• On-board Storage: Built-in storage capacity in an SBC.

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P

• Personal Health Records (PHRs): Health records maintained by the patient.
• Programmable Logic Controllers (PLCs): Programmable Logic Controllers (PLCs) are industrial-grade digital computers designed to perform control functions—especially in manufacturing and industrial processes. Initially developed to replace complex relay-based control systems, PLCs offer a flexible and efficient solution for automating tasks like machinery control, assembly line management, and other industrial operations. Read on
• Prescriptive Maintenance: Maintenance strategy that uses data analysis and diagnostics.
• Pre-training: The process of training a model on a large dataset in an unsupervised manner before fine-tuning it on a specific task. Pre-training helps the model learn general language representations.
• Private Project: A private project is an Edge Impulse project only viewable, modifiable and clonable by the user, collaborators and organization members.
• Project: An Edge Impulse project is an ML pipeline.
• Prompt Engineering: The process of designing input prompts to effectively utilize the capabilities of large language models to perform specific tasks.
• Public Project: A public project is an Edge Impulse project made public under the Apache 2.0 license with the community on the Edge Impulse community portal.
• PWM (Pulse Width Modulation): Technique for analog results with digital means.

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Q

• Quantization: A technique to reduce model weights and biases in numerical precision to save memory and speed up computation.

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R

• Raspberry Pi: A series of small SBCs for teaching computer science.
• Real-Time Operating System (RTOS): An OS designed for real-time applications.
• Real-Time Processing: Immediate processing of input for timely output.
• Remote Patient Monitoring (RPM): Recording and analyzing health data in real-time.

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S

• SCADA: System for remote monitoring and control.
• Self-Attention: A mechanism that allows the model to weigh the importance of different parts of the input sequence, enabling it to focus on relevant information when making predictions.
• Sensor Data: Data from physical sensors like temperature, motion, etc.
• Sensor Fusion: Combining data from multiple sensors to improve accuracy and reliability of machine learning models. Learn more.
• Sequence-to-Sequence (Seq2Seq): A type of model architecture used for tasks where an input sequence is transformed into an output sequence, such as translation or summarization.
• Smart Health: Advanced technologies in healthcare for monitoring and treatment.
• SoC (System on Chip): An integrated circuit integrating all components of a computer.
• SBC (Single Board Computer): A complete computer on one circuit board.

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T

• Telehealth: Health-related services via electronic technologies.
• TensorFlow: A set of software tools focused on deep learning, published by Google.
• LiteRT (previously Tensorflow Lite): A tool within TensorFlow that helps run inference on mobile and embedded Linux devices.
• LiteRT (previously Tensorflow Lite) for Microcontrollers: A tool within TensorFlow that helps run inference on bare metal devices such as microcontrollers.
• Tokenization: The process of converting a text into a sequence of tokens (words, subwords, or characters) that can be used as input for a language model.
• Tiny Machine Learning (TinyML): ML for low-power devices.
• Training: Teaching a machine learning model using data.
• Transfer learning: A special technique for training models that reduces the amount of data required.
• Transformer Model: A type of neural network architecture that uses self-attention mechanisms to process input data. Transformers are the foundation of many state-of-the-art LLMs, including BERT and GPT.

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U

• UART (Universal Asynchronous Receiver/Transmitter): Serial communication protocol.
• User: Designated license holder on the Edge Impulse platform, empowered to create and execute projects.

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W

• Wearable Technology: Devices collecting health and exercise data.

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X

• X86 Architecture: A common CPU architecture used in PCs.

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Y

• Yocto Project: Open-source project for creating custom Linux distributions.
• YOLO (You Only Look Once): Object detection architecture that outputs bounding boxes. Learn more.
• YOLOv4 (You Only Look Once version 4): Enhanced version of YOLO with improved accuracy and performance for object detection tasks.

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Z

• Zero-shot Learning: A machine learning paradigm where a model can make predictions on classes it has never seen during training.