Real-time edge nodes demand deterministic scheduling. This post shows how to tune Zephyr RTOS thread priorities, interrupt handling, and memory for multi-sensor workloads.
A practical guide to building a low-overhead eBPF runtime monitoring stack for edge Linux gateways, covering architecture, implementation with libbpf and BCC, and optimization strategies for constrained environments.
From your first Worker to a globally distributed application: a practical walkthrough of Cloudflare’s edge stack, the patterns that work in production, and the trade-offs you should know before going all-in.
A practical guide for engineers who need to run LLMs on edge hardware, covering pruning, quantization, and architecture patterns that keep latency low and memory tight.
Table of Contents Introduction Edge Intelligence & Ambient Computing: A Primer Why Small Language Models (SLMs) Are the Right Fit for the Edge Core Challenges When Running SLMs on Edge Devices Optimization Strategies for Real‑Time Edge Deployment 5.1 Quantization 5.2 Pruning & Structured Sparsity 5.3 Knowledge Distillation 5.4 Low‑Rank Factorization 5.5 Efficient Transformer Variants 5.6 On‑Device Compilation & Runtime Engines 5.7 Hardware‑Aware Neural Architecture Search (HW‑NAS) Practical Walk‑Through: Tiny Conversational Agent for a Smart‑Home Hub Real‑World Use Cases Monitoring, Updating, and Security at the Edge Future Directions: Federated & Continual Learning on Ambient Devices Conclusion Resources Introduction Edge intelligence—the ability to run sophisticated AI algorithms directly on devices that sit at the “edge” of a network—has moved from a research curiosity to a production necessity. From wearables that understand spoken commands to AR glasses that translate foreign text in real time, the demand for low‑latency, privacy‑preserving, and always‑on AI is exploding. ...