emza-iot visual sensor
Emza Visual Sense, an Israeli firm that offers ultra-low-power AI visual sensing solutions, was acquired by Synaptics Incorporated a publicly owned San Jose, California-based developer of human interface hardware and software. While the purchase price was not disclosed, Calcalist estimated the deal to be worth $10 million. Emza was acquired from its current parent company, Taiwan-based fabless semiconductor company Himax Technologies.
Based in Giv’atayim, near Tel Aviv, the Emza team will become part of Synaptics Israel, in Herzliya.
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Founded in 2006, Emza Visual Sense develops ultra-low power edge AI devices. The company provides solutions including hardware, software, algorithms and IP to semiconductor companies and OEMs bringing AI capability to tiny, power- and cost-constrained edge devices. As compute power increases and silicon costs decline, the market for these tiny edge AI devices is rapidly expanding across a broad array of segments such as consumer, industrial, automotive and smart cities.
Synaptics said that the Emza solution complements Synaptics’ KatanaTM AI SoC platform and together they enable HPD applications in devices ranging from PCs, notebooks, and smart TVs, to assisted living cameras.
“We’re very excited about integrating and scaling the Emza team’s expertise and technology across our business,” said Saleel Awsare, SVP & GM at Synaptics. “Bringing together key enabling technologies in edge hardware and algorithms for computer vision, audio, and security, while leveraging decades of experience and deep knowledge of customers’ requirements, will allow us to together rapidly deploy, scale, and re-define the HPD experience.”
A typical low-power smart vision architecture consists of a low-resolution image sensor coupled with an Edge AI SoC that is usually constrained with respect to compute and memory resources. Synaptics stated that already deployed and field proven for a multitude of applications, Emza’s uniquely tuned ML algorithms meet the challenges of such a resource-constrained environment by maximizing AI inference per milliwatt for optimal visual sensing performance.
