Embedded Systems

Table of Contents Introduction What Is an Embedded System? Core Architectural Elements 3.1 Microcontrollers vs. Microprocessors 3.2 Memory Hierarchy 3.3 Peripheral Interfaces Real‑Time Operating Systems (RTOS) Development Workflow 5.1 Toolchains and IDEs 5.2 Build Systems and Continuous Integration Programming Languages for Embedded 6.1 C and C++ 6.2 Rust 6.3 Python in Resource‑Constrained Environments Hardware Design Basics 7.1 Schematic Capture & PCB Layout 7.2 Power Management Strategies Communication Protocols 8.1 Serial Buses (UART, SPI, I²C) 8.2 Network‑Level Protocols (CAN, Ethernet, LoRa, MQTT) Security in Embedded Systems Case Studies 10.1 Automotive Control Units 10.2 Industrial IoT Sensors 10.3 Medical Wearables Testing, Debugging, and Certification 12 Future Trends 13 Conclusion 14 Resources Introduction Embedded systems are everywhere—from the tiny microcontroller that blinks an LED on a kitchen appliance to the sophisticated control units that keep autonomous cars on the road. Unlike general‑purpose computers, an embedded system is purpose‑built to perform a specific set of tasks, often under strict constraints on power, size, latency, and reliability. ...

Table of Contents Introduction Why Transformers on Embedded Devices? Memory‑Efficient Transformer Variants 3.1 DistilBERT & TinyBERT 3.2 MobileBERT 3.3 Linformer 3.4 Performer & FAVOR+ 3.5 Reformer 3.6 Quantized & Pruned Models Embedded Platforms & Toolchains Benchmark Design 5.1 Metrics to Capture 5.2 Datasets & Workloads 5.3 Measurement Methodology Implementation Walk‑Through 6.1 Preparing a Model with Hugging Face & ONNX 6.2 Converting to TensorFlow Lite (TFLite) 6.3 Deploying on a Cortex‑M55 MCU Experimental Results 7.1 Latency & Throughput 7.2 Memory Footprint 7.3 Energy Consumption 7.4 Accuracy Trade‑offs Interpretation & Best‑Practice Guidelines Future Directions Conclusion Resources Introduction Transformer models have become the de‑facto standard for natural language processing (NLP), computer vision, and increasingly for multimodal AI. Their self‑attention mechanism enables unprecedented performance on tasks ranging from language translation to object detection. However, the same architectural strengths that make transformers powerful also make them resource‑hungry: they demand gigabytes of RAM, billions of FLOPs, and high‑throughput memory bandwidth. ...