New Delhi (India), JANUARY 06: LivNSense
Avnish Kumar, CEO of LivNSense, stated, “We are very excited to partner with SOYNET to enable comprehensive AI-Vision technology of Future offerings for the Manufacturing industry. The product is distributed edge and cloud-based Computer Vision/AI solution to improve visibility, safety and sustainability in hazardous as well as normal environments. It is a real-time vision-based solution which can be extended to advanced analytics and AI model based with a self-learning automated approach. Soynet will also optimize our product to reduce the Carbon Footprint and improve the overall Carbon Offset for the use cases offered by GreenOps. It is already production deployed with a leading manufacturing company in North America and India and now expanding to users in South Korea.
Jung Woo Park, COO SOYNET, stated, “We have always envisioned helping and creating an ecosystem that can leverage SoyNet’s potential to deploy real-time AI. So much research goes into learning; however, when the AI models are deployed, they do not perform fast enough and consume a huge amount of memory. We want to help SMBs and Enterprises to accelerate their AI models and save huge GPU costs. Our partnership with LivNsense will bring better products for different industries and give them cost optimization up to 3 times for cloud deployment. We are thrilled to have LivNsense on board to help broaden this ecosystem in Asia, USA and global markets.”
About LivNSense Technologies Pvt. Ltd.
LivNSense
To learn more, visit https://www.livnsense.com
About SOYNET
SOYNET, established in South Korea in 2018, is a software-based inference accelerator developer. Our proprietary solution, SoyNet, provides model optimization for deep learning. We have also introduced Model Market, the first-ever marketplace for SoyNet-optimized models. These deep learning models are, on average, 6 times faster and consume around 3 times less memory than public frameworks like TensorFlow, Pytorch and TensorRT. With the help of NVIDIA’s SDK, SoyNet ensures the optimized models consume significantly less GPU memory so that you can run multiple models on a lower-end GPU. Skynet’s optimized models are provided in a bin folder/Docker file that can be quickly executed and deployed on the cloud, on-premises or on edge devices. Using the 5-step API process, the optimized models are easily integrated with C++, Java or Python applications.
To learn more, visit SOYNET
Main Focus Area of this Joint Partnership
Accelerate with AI-VISION Based Technology for Manufacturing Industry
Reduce Carbon Emission for future-ready of Industry
Optimize Products with Zero Carbon Footprint to enable Net Zero
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