A practical guide to building low-latency sensor fusion on ESP32 with FreeRTOS and Kalman filters, covering task design, noise modeling, and real-world performance tuning.
How to design and ship eBPF-based telemetry pipelines on constrained embedded Linux devices — covering CO-RE, ring buffers, perf events, and the patterns that survive contact with 32 MB RAM and flaky flash.
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 Edge Inference? A Shift from Cloud APIs Fundamental Challenges of Running SLMs on the Edge Optimization Techniques that Make Local Inference Viable 4.1 Quantization 4.2 Pruning & Structured Sparsity 4.3 Knowledge Distillation 4.4 Weight Sharing & Low‑Rank Factorization 4.5 On‑Device Compilation & Runtime Tricks A Hands‑On Example: Deploying a 7‑B SLM on a Raspberry Pi 5 End‑to‑End Deployment Workflow Security, Privacy, and Regulatory Benefits of Local Inference Real‑World Use Cases Driving the Adoption Curve Future Directions: Tiny‑SLMs, Neuromorphic Chips, and Beyond Conclusion Resources Introduction Large language models (LLMs) have transformed how software interacts with natural language—everything from chat assistants to code generation. Historically, the sheer computational demand of these models forced developers to rely on cloud‑hosted APIs (OpenAI, Anthropic, Cohere, etc.). While cloud APIs provide a low‑friction entry point, they carry latency, bandwidth, cost, and privacy penalties that become untenable for edge devices such as drones, wearables, industrial controllers, and IoT gateways. ...
Table of Contents Introduction Why Local Inference Matters Characteristics of Small Language Models Edge & IoT Constraints You Must Respect Model Selection Strategies Quantization: From FP32 to INT8/INT4 Pruning and Knowledge Distillation Runtime Optimizations & Hardware Acceleration Deployment Pipelines for Edge Devices Security, Privacy, and Governance Real‑World Case Studies Best‑Practice Checklist Conclusion Resources Introduction The explosion of large language models (LLMs) has transformed natural‑language processing (NLP) across cloud services, but the same power is increasingly demanded at the edge: on‑device sensors, industrial controllers, autonomous drones, and privacy‑sensitive wearables. Running inference locally eliminates latency spikes, reduces bandwidth costs, and—most importantly—keeps user data under the owner’s control. ...