Mark Oliver, the VP of Marketing at Efinix demonstrates how multiple AI models can be compiled to run on dedicated AI accelerators implemented in the high performance Titanium FPGA family. He shows how Efinix supplied tools can be used to optimize an AI model to run on an AI accelerator delivering hardware level performance while […]
Mark Oliver, the VP of Marketing at Efinix demonstrates the ability to run four independent AI models on the hardened quad core processor inside the Titanium family of FPGAs. He shows how an intuitive software flow can be accelerated through custom instructions to run “bottle neck” software routines in the FPGA fabric at hardware speed
Mark Oliver Demonstrates the Power of Custom Instruction Acceleration for Edge AI Read More +
This demonstration by MosChip and Cadence shows a Small Language Model (SLM) voice assistant running entirely on-device on the Cadence Tensilica Vision Q7 DSP within an Axera AX650N platform – with no cloud connection. It walks through the full interaction loop: spoken input is converted to text, a compact quantized language model (SLM) generates
Suresh Pasupuleti, Managing Director of Vedya Labs, presents the company’s work in bringing Stable Diffusion-based image generation to DSP-centric embedded platforms. The demonstration showcases a nearly 500-million-parameter model running on the Axera AX650N SoC, with the text encoder, U-Net, and VAE stages optimized for dual Cadence Tensilica Vision DSPs. Using INT8 quantization and a combination
Vedya Labs Demonstration of Stable Diffusion Deployment on Cadence Tensilica DSPs Read More +
Mauricio Greene of Bolom demonstrates real-time Sound Classification running on the Cadence Tensilica HiFi 5 DSP at the Embedded Vision Summit. Bolom Acoustic Intelligence edge models identify hundreds of distinct sound events and soundscape scenes – such as sirens, alarms, horns, traffic and more, fully on-device and without relying on the cloud. The Tensilica HiFi
Bolom Sound Classification on Cadence Tensilica HiFi 5 Read More +
This blog post was originally published at Ambarella’s website. It is reprinted here with the permission of Ambarella. The orbital community has reached the same conclusions that the broader edge AI industry has been articulating for years: If moving the data is more expensive than moving the result, the processing belongs where the data was produced. The
When the Edge Is 400 Kilometers Up: AI, Space, and the Limits of Cloud Computing Read More +
This content was originally published at HTEC’s website. It is reprinted here with the permission of HTEC. Physical AI at the Edge: Building the Full Stack for Real-World Deployment For years, AI progress was measured by model benchmark scores. The real test is different: does it work when deployed in a vehicle, a factory, a
This blog post was originally published at Helbling’s website. It is reprinted here with the permission of Helbling. The full value of Machine Learning (ML) and Artificial Intelligence (AI) only emerges when the entire lifecycle of an application is taken into account. Yet many companies struggle to establish a sustainable and scalable operating model early enough
Successful Machine Learning Projects Shape the Entire Lifecycle Read More +
Pete Warden, CEO at Moonshine AI presents “Building a Local Voice Agent on a Raspberry Pi” at the May 2026 Embedded Vision Summit. In this talk, Warden explains everything you need to know to build your own voice agent running entirely on a stock Raspberry Pi 5, with no internet… “Building a Local Voice Agent
“Building a Local Voice Agent on a Raspberry Pi,” a Presentation from Moonshine AI Read More +
This blog post was originally published at ModelCat’s website. It is reprinted here with the permission of ModelCat. Artificial intelligence systems often appear highly effective during development. Models achieve strong benchmark scores, validation metrics improve over time, and performance looks predictable within controlled environments. But once those same systems are deployed into real-world conditions, results frequently
Why Most AI Performance Metrics Break Down in Production Read More +
Here you’ll find a wealth of practical technical insights and expert advice to help you bring AI and visual intelligence into your products without flying blind.
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