tag: NPUs

AI Models On The Edge
By Ed Sperling - 14 Jul, 2026 - Comments: 0

Moving from large language models in the cloud to small language models at the edge is much more complicated than just slimming down the algorithms. It requires changes in both hardware and software, and the constraints can vary greatly from one market segment to another. Daniel Firu, CPO and co-founder of Quadric, and Ravi Chakaravarthy, vice president of software at the company, talk about ho... » read more

Benchmarking An NPU At Scale
By Marc Meszaros - 09 Jul, 2026 - Comments: 0

With every Chimera SDK release, something quietly industrial happens: the entire 300+ model zoo gets automatically recompiled and re-profiled across a broad sweep of hardware configurations and the results land in DevStudio in the process. No one is hand-typing FPS numbers into a slide or running a single model on a single configuration to cherry-pick for a datasheet. The whole zoo moves forwar... » read more

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

The Edge LLM Offload Story
By Karthikeyan Shanmuga Vadivel - 04 Jun, 2026 - Comments: 0

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

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

Vision-Language-Action Models Arrive
By Mike Leonard - 14 May, 2026 - Comments: 0

The AI model type capturing the most attention across robotics and autonomous vehicles right now is the vision-language-action model, or VLA. At embedded AI conferences this year, particularly the recently held Embedded Vision Summit, VLAs were a main topic of discussion – not as a research curiosity, but as the architecture that teams building autonomous systems are actively targeting. If yo... » read more

Why More CPUs Are Needed For Agentic AI
By Ed Sperling - 15 Apr, 2026 - Comments: 0

The shift from generative AI to agentic AI will significantly increase the amount of compute power needed in data centers. Queries to search for and analyze data from multiple sources will be performed simultaneously by agents and without human intervention, rather than a single request from a live person. Jeff Defilippi, senior director of product management at Arm, talks about the impact of r... » read more

Heterogeneous NPU Data Movement: What The Execution Flow Shows
By Steve Roddy - 14 Apr, 2026 - Comments: 0

Heterogeneous NPU designs bring together multiple specialized compute engines to support the range of operators required by modern AI models. This approach enables coverage across diverse workloads, but it also introduces a structural consequence: intermediate data must move between those engines. That movement consumes power, adds latency, and requires additional silicon resources, with effect... » 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

AI Accelerators Usher In New Era For IC Test
By Laura Peters - 07 Apr, 2026 - Comments: 0

Key Takeaways The parallelism in AI accelerators enables low latency but complicates failure isolation. HBM can account for 50% of package cost, so known-good stack assurance is critical. DFT and test cooperate to solve final test, singulated die test, SLT, and in-system test for data centers. AI accelerators are used for everything from training large language models to mak... » read more

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