FPGA vs ASIC for AI at the Edge: What factors influence your hardware choice?
As AI continues to move closer to edge devices, choosing the right hardware platform has become an important design decision. While both FPGAs and ASICs have their strengths, the best choice often depends on the application's requirements. Here are some of the key factors that engineering teams typically evaluate: Performance and latency requirements Power efficiency Development cost and NRE Time-to-market Production volume Need for future hardware updates FPGAs offer flexibility for rapid prototyping and evolving workloads, making them well-suited for early-stage development. ASICs, on the other hand, can provide significant advantages in performance, power consumption, and cost efficiency for high-volume production. I recently came across a technical article that explains these trade-offs in a structured way and found it useful as a reference: https://www.signoffsemiconductors.com/asic-vs-fpga/ I'd be interested to hear how others approach this decision. Have you migrated a design from FPGA to ASIC? What factors influenced your choice? Are there workloads where you would always choose one over the other?From Cloud to Chip: Building Smarter AI at the Edge with Windows AI PCs
As AI engineers, we’ve spent years optimizing models for the cloud, scaling inference, wrangling latency, and chasing compute across clusters. But the frontier is shifting. With the rise of Windows AI PCs and powerful local accelerators, the edge is no longer a constraint it’s now a canvas. Whether you're deploying vision models to industrial cameras, optimizing speech interfaces for offline assistants, or building privacy-preserving apps for healthcare, Edge AI is where real-world intelligence meets real-time performance. Why Edge AI, Why Now? Edge AI isn’t just about running models locally, it’s about rethinking the entire lifecycle: - Latency: Decisions in milliseconds, not round-trips to the cloud. - Privacy: Sensitive data stays on-device, enabling HIPAA/GDPR compliance. - Resilience: Offline-first apps that don’t break when the network does. - Cost: Reduced cloud compute and bandwidth overhead. With Windows AI PCs powered by Intel and Qualcomm NPUs and tools like ONNX Runtime, DirectML, and Olive, developers can now optimize and deploy models with unprecedented efficiency. What You’ll Learn in Edge AI for Beginners The Edge AI for Beginners curriculum is a hands-on, open-source guide designed for engineers ready to move from theory to deployment. Multi-Language Support This content is available in over 48 languages, so you can read and study in your native language. What You'll Master This course takes you from fundamental concepts to production-ready implementations, covering: Small Language Models (SLMs) optimized for edge deployment Hardware-aware optimization across diverse platforms Real-time inference with privacy-preserving capabilities Production deployment strategies for enterprise applications Why EdgeAI Matters Edge AI represents a paradigm shift that addresses critical modern challenges: Privacy & Security: Process sensitive data locally without cloud exposure Real-time Performance: Eliminate network latency for time-critical applications Cost Efficiency: Reduce bandwidth and cloud computing expenses Resilient Operations: Maintain functionality during network outages Regulatory Compliance: Meet data sovereignty requirements Edge AI Edge AI refers to running AI algorithms and language models locally on hardware, close to where data is generated without relying on cloud resources for inference. It reduces latency, enhances privacy, and enables real-time decision-making. Core Principles: On-device inference: AI models run on edge devices (phones, routers, microcontrollers, industrial PCs) Offline capability: Functions without persistent internet connectivity Low latency: Immediate responses suited for real-time systems Data sovereignty: Keeps sensitive data local, improving security and compliance Small Language Models (SLMs) SLMs like Phi-4, Mistral-7B, Qwen and Gemma are optimized versions of larger LLMs, trained or distilled for: Reduced memory footprint: Efficient use of limited edge device memory Lower compute demand: Optimized for CPU and edge GPU performance Faster startup times: Quick initialization for responsive applications They unlock powerful NLP capabilities while meeting the constraints of: Embedded systems: IoT devices and industrial controllers Mobile devices: Smartphones and tablets with offline capabilities IoT Devices: Sensors and smart devices with limited resources Edge servers: Local processing units with limited GPU resources Personal Computers: Desktop and laptop deployment scenarios Course Modules & Navigation Course duration. 10 hours of content Module Topic Focus Area Key Content Level Duration 📖 00 Introduction to EdgeAI Foundation & Context EdgeAI Overview • Industry Applications • SLM Introduction • Learning Objectives Beginner 1-2 hrs 📚 01 EdgeAI Fundamentals Cloud vs Edge AI comparison EdgeAI Fundamentals • Real World Case Studies • Implementation Guide • Edge Deployment Beginner 3-4 hrs 🧠 02 SLM Model Foundations Model families & architecture Phi Family • Qwen Family • Gemma Family • BitNET • μModel • Phi-Silica Beginner 4-5 hrs 🚀 03 SLM Deployment Practice Local & cloud deployment Advanced Learning • Local Environment • Cloud Deployment Intermediate 4-5 hrs ⚙️ 04 Model Optimization Toolkit Cross-platform optimization Introduction • Llama.cpp • Microsoft Olive • OpenVINO • Apple MLX • Workflow Synthesis Intermediate 5-6 hrs 🔧 05 SLMOps Production Production operations SLMOps Introduction • Model Distillation • Fine-tuning • Production Deployment Advanced 5-6 hrs 🤖 06 AI Agents & Function Calling Agent frameworks & MCP Agent Introduction • Function Calling • Model Context Protocol Advanced 4-5 hrs 💻 07 Platform Implementation Cross-platform samples AI Toolkit • Foundry Local • Windows Development Advanced 3-4 hrs 🏭 08 Foundry Local Toolkit Production-ready samples Sample applications (see details below) Expert 8-10 hrs Each module includes Jupyter notebooks, code samples, and deployment walkthroughs, perfect for engineers who learn by doing. Developer Highlights - 🔧 Olive: Microsoft's optimization toolchain for quantization, pruning, and acceleration. - 🧩 ONNX Runtime: Cross-platform inference engine with support for CPU, GPU, and NPU. - 🎮 DirectML: GPU-accelerated ML API for Windows, ideal for gaming and real-time apps. - 🖥️ Windows AI PCs: Devices with built-in NPUs for low-power, high-performance inference. Local AI: Beyond the Edge Local AI isn’t just about inference, it’s about autonomy. Imagine agents that: - Learn from local context - Adapt to user behavior - Respect privacy by design With tools like Agent Framework, Azure AI Foundry and Windows Copilot Studio, and Foundry Local developers can orchestrate local agents that blend LLMs, sensors, and user preferences, all without cloud dependency. Try It Yourself Ready to get started? Clone the Edge AI for Beginners GitHub repo, run the notebooks, and deploy your first model to a Windows AI PC or IoT devices Whether you're building smart kiosks, offline assistants, or industrial monitors, this curriculum gives you the scaffolding to go from prototype to production.Understanding Small Language Modes
In Part 1, we discussed the differences between Large Language Models (LLMs) and Small Language Models (SLMs), highlighting how SLMs are transforming edge computing. Unlike LLMs, which rely on massive cloud infrastructure, SLMs can run directly on local devices like smartphones and IoT systems, offering faster performance, better privacy, and lower energy use. In this second part, we dive into how SLMs work, exploring their technical foundations and design principles that make them ideal for edge environments.Understanding Small Language Modes
Small Language Models (SLMs) bring AI from the cloud to your device. Unlike Large Language Models that require massive compute and energy, SLMs run locally, offering speed, privacy, and efficiency. They’re ideal for edge applications like mobile, robotics, and IoT.
Built a Real-Time Azure AI + AKS + DevOps Project – Looking for Feedback
Hi everyone, I recently completed a real-time project using Microsoft Azure services to build a cloud-native healthcare monitoring system. The key services used include: Azure AI (Cognitive Services, OpenAI) Azure Kubernetes Service (AKS) Azure DevOps and GitHub Actions Azure Monitor, Key Vault, API Management, and others The project focuses on real-time health risk prediction using simulated sensor data. It's built with containerized microservices, infrastructure as code, and end-to-end automation. GitHub link (with source code and documentation): https://github.com/kavin3021/AI-Driven-Predictive-Healthcare-Ecosystem I would really appreciate your feedback or suggestions to improve the solution. Thank you!Scaling Smart with Azure: Architecture That Works
Hi Tech Community! I’m Zainab, currently based in Abu Dhabi and serving as Vice President of Finance & HR at Hoddz Trends LLC a global tech solutions company headquartered in Arkansas, USA. While I lead on strategy, people, and financials, I also roll up my sleeves when it comes to tech innovation. In this discussion, I want to explore the real-world challenges of scaling systems with Microsoft Azure. From choosing the right architecture to optimizing performance and cost, I’ll be sharing insights drawn from experience and I’d love to hear yours too. Whether you're building from scratch, migrating legacy systems, or refining deployments, let’s talk about what actually works.
How integreate Azure IoT Hub with Azure Synapse in RealTime
Hello, I'm researching how to connect Azure IoT Hub with Azure Synapse, I've already used IoT Hub a bit but I don't have any knowledge of Synapse, it is also required that the data be in RT, so if someone has already done something similar or knows where I can find answers I would appreciate it. Have a good day.Develop modern connected apps with the MEC Accelerator and 5G Kit
Today, 5G enables new applications for scenarios that were previously out of reach. From smart roads that can notify of obstacles in the road in real time to smart airports that can identify runway issues before they threaten safety, a new breed of real-time and mission-critical applications is emerging. Developers can now start building their own MEC/Edge application by forking and evolving our open source MEC Application Solution Accelerator. This is an example application that offers a common foundation based on a light microservices architecture designed for the edge. It includes Kubernetes, docker containers, and Dapr framework. It also includes AI model inferencing capabilities with an example deep learning model that analyzes video coming from cameras, as well as MQTT event-driven communication following a publish/subscription pattern to raise events/alerts after detecting issues with the AI models. Who should attend? • Developers interested in building real-time and mission-critical applications for the edge with low latency. • Individuals interested in learning about AI model inferencing capabilities and deep learning models for analyzing video. • Professionals seeking to learn about light/fast event-driven communication between AI models events/alerts handlers. What will I learn? • How to build MEC/Edge applications using the open source MEC Application Solution Accelerator. • Understanding and get ready to get started on a light microservices and event-driven architecture, based on Kubernetes, Docker containers, Dapr framework and MQTT messaging broker for edge/mec applications with low latency. • The potential of 5G technology and how it enables new applications for previously unreachable scenarios. Pre-Register at: https://aka.ms/ModernConnectedApplications05.16 Watch Livestream at: https://aka.ms/ModernConnectedApplications5/15
Former Employer Abuse
My former employer, Albert Williams, president of American Security Force Inc., keeps adding my outlook accounts, computers and mobile devices to the company's azure cloud even though I left the company more than a year ago. What can I do to remove myself from his grip? Does Microsoft have a solution against abusive employers?
Creating Logic App to Identify Low Storage Devices from Intune
Hello everyone, I’m seeking some assistance with creating a Logic App. I need to identify devices in Intune that have 5GB or less of available space and receive an email with the details of these devices, including their names. Is this achievable?
