Here are our picks for the top 10 edge AI chips with a bright future across applications from vision processing to handling multimodal LLMs.
Many decisions cannot wait for a round trip to the cloud. Driver monitoring, industrial sensing and adaptive audio all ...
The AI landscape is taking a dramatic turn, as small language and multimodal models are approaching the capabilities of larger, cloud-based systems. This acceleration reflects a broader shift toward ...
‘Hey Google’ find me a suitable keyword spotting (KWS) model for edge devices. While voice control is essential for modern interfaces like Alexa, Siri, and Hey Google, building KWS models on edge ...
SAN JOSE, Calif.--(BUSINESS WIRE)--Edge Impulse, the leading platform for building, deploying, and scaling edge machine learning models, today announces Microchip Technology’s SAMA7G54 microprocessor ...
TOKYO--(BUSINESS WIRE)--Mitsubishi Electric Corporation (TOKYO: 6503) announced today that it has developed a language model tailored for manufacturing processes operating on edge devices. The Maisart ...
New developments that will leverage a focus on cloud-native technologies, including serverless and containers, will change the level of I/O requirements. Internet egress (i.e., the on-ramps and ...
ExecuTorch 1.0 allows developers to deploy PyTorch models directly to edge devices, including iOS and Android devices, PCs, and embedded systems, with CPU, GPU, and NPU hardware acceleration.
The emergence of DeepSeek as a transformative force in edge AI development has supply chain operators anticipating a new wave of consumer upgrades across smart devices, from smartphones to AI PCs.
Artificial intelligence (AI) and machine learning (ML) have undergone significant transformations over the past decade. The revolution of convolutional neural networks (CNNs) and recurrent neural ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results