category: Inside Edge AI Processing

category: Auto, Security & Edge AI

How To Start Building Edge-Native AI
By Sharad Chole - 11 Jun, 2026 - Comments: 0

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

Why Vision LLMs Force A Rethink Of Edge AI Hardware
By Athish Rao - 14 May, 2026 - Comments: 0

As vision-centric large language models move on-device, performance measured in raw TOPS is no longer enough. Architectures need to be built around real workloads, memory behavior, and sustained utilization, especially at the edge. Vision LLMs are changing the edge AI equation For the last decade, most edge AI silicon has been built to do one job extremely well: run convolutional networks for... » read more

The Coming Breakup Between AI And The Cloud
By Sharad Chole - 09 Apr, 2026 - Comments: 0

For a decade, cloud AI has felt inevitable. It powers our voice assistants, photo libraries, recommendation engines, and a growing list of “smart” features we barely notice anymore. Yet beneath the convenience is a fragile dependency: if your connection stutters, your intelligence does too.​ We rarely question this arrangement, but we should. As models grow larger and expectations grow... » read more

Next Generation AI: Transitioning Inference From The Cloud To The Edge
By Paul Karazuba - 11 Dec, 2025 - Comments: 0

AI inference deployments are increasingly focused on the edge as manufacturers seek the consistent latency, enhanced privacy, and reduced operational costs they can’t achieve in cloud-based deployments. While cloud-based platforms provide incredible computational power and enable widely adopted services, the dependence on network connectivity inherently creates variability, cost and security ... » read more

Transformers At The Edge: Efficient LLM Deployment
By Paul Karazuba - 17 Jul, 2025 - Comments: 0

Since the groundbreaking 2017 publication of “Attention Is All You Need,” the transformer architecture has fundamentally reshaped artificial intelligence research and development. This innovation laid the foundation for Large Language Models (LLMs) and Video Language Models (VLMs), fueling a wave of productization across the industry. A defining milestone was the public launch of ChatGPT in... » read more

NPU Acceleration For Multimodal LLMs
By Pat Donnelly - 12 Dec, 2024 - Comments: 0

Transformer-based models have rapidly spread from text to speech, vision, and other modalities. This has created challenges for the development of Neural Processing Units (NPUs). NPUs must now efficiently support the computation of weights and propagation of activations through a series of attention blocks. Increasingly, NPUs must be able to process models with multiple input modalities with ac... » read more

Small Language Models: A Solution To Language Model Deployment At The Edge?
By Paul Karazuba - 18 Nov, 2024 - Comments: 0

While Large Language Models (LLMs) like GPT-3 and GPT-4 have quickly become synonymous with AI, LLM mass deployments in both training and inference applications have, to date, been predominately cloud-based. This is primarily due to the sheer size of the models; the resulting processing and memory requirements often overwhelm the capabilities of edge-based systems. While the efficiency of Exped... » read more

Vision Is Why LLMs Matter On The Edge
By Ben Gomes - 30 May, 2024 - Comments: 0

Large Language Models (LLMs) have taken the world by storm since the 2017 Transformers paper, but pushing them to the edge has proved problematic. Just this year, Google had to revise its plans to roll out Gemini Nano on all new Pixel models — the down-spec’d hardware options proved unable to host the model as part of a positive user experience. But the implementation of language-focused mo... » read more

Considerations For Accelerating On-Device Stable Diffusion Models
By Pat Donnelly - 30 Nov, 2023 - Comments: 0

One of the more powerful – and visually stunning – advances in generative AI has been the development of Stable Diffusion models. These models are used for image generation, image denoising, inpainting (reconstructing missing regions in an image), outpainting (generating new pixels that seamlessly extend an image's existing bounds), and bit diffusion. Stable Diffusion uses a type of dif... » read more

Unlocking The Power Of Edge Computing With Large Language Models
By Paul Karazuba - 30 Oct, 2023 - Comments: 0

In recent years, Large Language Models (LLMs) have revolutionized the field of artificial intelligence, transforming how we interact with devices and the possibilities of what machines can achieve. These models have demonstrated remarkable natural language understanding and generation abilities, making them indispensable for various applications. However, LLMs are incredibly resource-intensi... » read more

← Older posts

  • Sponsors

About

Navigation

Connect With Us

Copyright ©2013-2026 SMG   |  Terms of Service  |  Privacy Policy