Avalanche Computing develops hyper-scale computing technique for speed and high-Scalability AI projects - The EE

Avalanche Computing develops hyper-scale computing technique for speed and high-Scalability AI projects

Jay Chen of Avalanche Computing

CES 2021 – Avalanche Computing, a hyper-scale computing technique provider based in Taipei and the Bay Area, has developed a one-click-through AI framework that allows for production speed and is highly scalable.

Avalanche Computing’s AI framework effectively trains your AI model in parallel or distributed mode on multiple GPUs without changing the algorithm for reducing AI project costs, and then does AI model deployment from one to many edge devices in one command.

Avalanche Computing focuses on optimising the deep learning environment and training in parallel, lightening the trained model, and then deploying the AI model at a hyper-scale. The proposed AI framework helps increase the speed of manufacturing and medical applications, including predictive maintenance, anomaly detection, machine diagnosis, defect detection, supply chain analysis, operation optimisation, EGG signal, breath sound, medical signal, medical speech to text, anomaly detection, lung cancer detection, and more.

Turnkey solution for AI development and deployment

“Our mission is to help our customers who focus on intelligence services and applications save time, staff numbers, and money during AI development. With our deep tech and computing domain knowledge, we aim to provide an excellent workflow for AI development and deployment in one platform at scale. Besides, our many alliance partners, academic collaborations, and computing hardware partners have further helped our AI customers succeed in global business,” says Avalanche founder and CEO Jay Chen.

“With our AI experience with global enterprises, we can help customers achieve high-speed AI training, reduce waiting time, and quickly scale the inference system without building a new engineer team.”

Developing AI to suit different needs across different industries

Chen says his company currently serves more than 15 enterprise-level manufacturing and medical AI companies and SMEs. Avalanche’s focus markets include the USA, Japan, Taiwan, and a several European countries, with target sectors including smart manufacturing, Industry 4.0, smart medical, and more. He related that his company has worked globally with NVidia SA, cloud service providers, and many software partners as well as with two accelerators, including NTUTEC and NTPC-AWS Joint Innovation Centre.

Avalance began tactical planning for expanding into the Japan and US markets in 2019. This year (2020), the company joined the NTU Corporate Accelerator Program (with AUO), TSS Lineup, and NVidia Inception Program; won AIdea first prizes in many categories; and was selected as an Airbus DDMS International Startups top-10 finalist. Avalanche Computing was selected by Taiwan Tech Arena (TTA) as one of the 100 featured Taiwanese startups showcased at CES 2021.

A significant team with high potential

Avalanche Computing Taiwan was founded by MOST LEAP members, NVidia alumni, professors, and serial entrepreneurs who embrace the company’s mission to help businesses without sufficient AI expertise to implement AI and transform the world together.

Avalance Computing co-founder Jay Chen earned his Ph.D. in Computer Science at National Taiwan University. His research interests include big data analytics, cloud computing, and mobile data analysis. He was involved in deep learning and AI framework performance optimisation at Nvidia. He has over 15 years of experience in big data analysis. He is a Phi Tau Phi Scholastic Honor Society member (Award in 2006 and 2016) and co-founder of the NTU Design Thinking Club. At Avalanche Computing, Jay provides technical support for smart manufacturing, smart medical, and more.

Avalanche Computing co-founder, C.W. Huang earned his master’s degree at National Taiwan University as well. His research thesis focused on machine learning, computer vision, and data mining. He has competed in numerous machine learning competitions and earned many recognitions, including a silver medal in the 4037-participant “Quora Insincere Question Classification” challenge hosted by Kaggle and a bronze medal in the “APTOS 2019 Blindness Detection” competition. At Avalanche, he developed a real-time breathing sound detection AI system in partnership with a smart medical technology corporation.

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