Wi-Fi Flies Higher As Edge AI Build-Out Takes Root


Key Takeaways: Wi-Fi 7 is becoming an essential technology for edge AI, and subequent demands for even better reliability will grow as the edge build-out takes shape. Edge computing is based on the assumption that more data will be processed and stored locally, which will help reduce data leakage and theft. The big challenge ahead will be orchestrating data movement across different ... » read more

How To Start Building Edge-Native AI


Cloud AI enables features like voice assistants and recommendations via centralized data centers, but it relies on consistent network connectivity, which often fails in real-world conditions. Edge-native AI shifts inference to devices such as phones, cars, and sensors, enabling real-time processing, enhanced privacy, and operational resilience. Why edge AI outpaces cloud Edge AI addresses key... » read more

Building Edge AI with IP Solutions


As AI inference moves from centralized cloud infrastructure into vehicles, factories, medical devices, and industrial systems, the decisive design challenge shifts from model quality to field-ready implementation. Deployed edge AI systems must perform reliably under a range of constraints, including fixed power budgets, stringent latency requirements, limited or intermittent cloud connectivity,... » read more

Beyond The Demo: Deploying And Evaluating Open-Source AI Workloads


As more open-source AI models move closer to real-world adoption, developers are changing how they evaluate edge deployment. The question is no longer simply whether a model can run, but whether it can be deployed reproducibly on a concrete platform, observed in practice, and turned into meaningful deployment decisions based on actual technical evidence. For developers, the CIX Armv9 platfor... » read more

PCIe Benefits From AI, Despite Scaling Protocols


Key takeaways: PCIe remains a critical technology for non-AI processing. For AI, PCIe will be strengthened by scale-out, agentic AI, and even some scale-up. CXL is seeing uptake, and some even think it could participate in AI processing. PCIe has been the go-to network for most data traffic moving from a processor to devices located elsewhere, which is also what the new data... » read more

Beyond PCIe Compliance: Why Stress Testing Is Crucial For Edge AI Deployments


Passing PCI Express (PCIe) compliance is different from being ready for the field. A PCIe link can clear every test in a controlled lab environment and still develop margin problems six months into deployment. That’s because a compliance traffic generator isn’t designed to replicate real-world operating conditions, such as thermal stress, electrical noise, and the kind of bursty inference t... » read more

The Edge LLM Offload Story


By Karthikeyan Shanmuga Vadivel and Sauryadeep Pal Developers and system architects today face a growing demand to enable large language model variants on device. They are facing pressure to support transformer-capable models on constrained devices to ensure data privacy, eliminate cloud API charges, and provide offline reliability. On-device execution is also becoming a necessity to meet st... » read more

Connectivity and Compute in Next-Gen Edge Devices


AI-native Edge devices are reshaping IoT by converging AI, connectivity and compute into a single platform. This paper highlights how Synaptics SYN765x brings Wi-Fi 7, local AI processing and intelligent sensing together to reduce latency, lower costs, enhance privacy and accelerate next-generation connected device design. Read more here. » read more

Beating the Edge AI Power Wall with Low Voltage Foundation IP


Edge AI is pushing the limits of power efficiency as intelligence moves closer to the data source. Designing for ultra-low voltage operation is now essential to achieve optimal performance-per-watt—but it introduces significant complexity in modeling, variation, and design predictability. In this white paper, discover how a unified, silicon-proven Foundation IP platform approach enables relia... » read more

Flexible AI-MCU For Fast Inference of Transformer Models At The Ultra-Low-Power Edge (ETH Zurich, U. Bologna)


Researchers from ETH Zurich and University of Bologna have released “CHIMERA: A Flexible and Scalable 3.1 TOPS/W AI-MCU with Transformer Accelerator and 563 Gb/s Shared-L2 Memory Subsystem with QoS Guarantees”. Abstract “We present Chimera, a flexible and scalable Microcontroller Unit (MCU) designed to accelerate real-time inference of rapidly evolving transformer-based models a... » read more

← Older posts