Neurala upgrades its VIA platform for faster deployment of AI models at the edge
Neurala, a company specializing in vision AI software, has unveiled a new version of its Neurala VIA platform designed to streamline the deployment of AI models. This latest iteration leverages the Lattice sensAI solution stack and low power Lattice FPGAs, with the aim of enabling developers to swiftly deploy AI models at the edge.
The company notes that the partnership represents an important step forward in advancing edge AI capabilities.
Dr. Max Versace, CEO and co-founder of Neurala, emphasizes the increasing importance of running AI at the edge to minimize power consumption, latency, bandwidth usage, and enhance data privacy.
“We’ve many years’ experience making it easy for our clients to create, deploy and customize AI at the edge on a variety of hardware from PCs, to smart phones, all the way to specialized processors and imaging sensors,” says Versace.
“Leveraging Lattice sensAI, designed to speed customer development and deployment of always-on, on-device AI into a wide range of edge applications, coupled with this new version of Neurala VIA, it makes deploying AI at the edge and maximizing the use of the available compute, easier than ever before.”
According to the company, by harnessing Lattice’s FPGA-based machine learning solutions and Neurala’s VIA platform, developers gain access to a versatile toolset for deploying AI models efficiently at the edge.
Matt Dobrodziej, VP of segment marketing at Lattice Semiconductor, highlights the significance of efficiency in edge AI computing as AI continues to revolutionize numerous markets and applications.
“This collaboration with Neurala is a great example of how Lattice’s low power FPGA technology and sensAI solution stack can help accelerate development cycles and enable designers to build and deploy scalable edge AI applications,” says Dobrodziej.
The collaboration aims to address the escalating demand for efficiency, lower power consumption, and enhanced data privacy in AI, as well as enables developers to rapidly develop and test deep learning applications directly at the edge.
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