Israel startup Run:AI has announced the completion of a $13 million financing round for its virtualization and acceleration solution for deep learning. Run:AI says that it bridges the gap between data science and computing infrastructure by creating a high performance compute virtualization layer for deep learning, speeding up the training of neural network models and enabling the development of huge AI models. The funding included a $10 million Series A round led by Haim Sadger's S Capital and TLV Partners, and a seed round of $3 million from TLV Partners.
Run:AI has completely rebuilt the software stack for deep learning to get past the limits of traditional computing, making training massively faster, cheaper and more efficient. It does this by virtualizing many separate compute resources into a single giant virtual computer with nodes that can work in parallel.
Run:AI was founded by CEO Omri Geller, Dr. Ronen Dar, and Prof. Meir Feder.
Geller said, "Traditional computing uses virtualization to help many users or processes share one physical resource efficiently; virtualization tries to be generous. But a deep learning workload is essentially selfish since it requires the opposite: it needs the full computing power of multiple physical resources for a single workload, without holding anything back. Traditional computing software just can't satisfy the resource requirements for deep learning workloads."
The company's software is tailored for these new computational workloads. The low-level solution works "close to the metal", taking full advantage of new AI hardware. It creates a compute abstraction layer that automatically analyzes the computational characteristics of the workloads, eliminating bottlenecks and optimizing them for faster and easier execution using graph-based parallel computing algorithms. It also automatically allocates and runs the workloads. This makes deep learning experiments run faster, lowers GPU costs, and maximizes server utilization while simplifying workflows.
Behind the scenes, Run:AI uses advanced mathematics to break up the original deep learning model into multiple smaller models that run in parallel. This has the additional benefit of bypassing memory limits, letting companies run models that are bigger than the GPU RAM that they usually have available.
Rona Segev-Gal, Managing Partner of TLV Partners, said, "Executing deep neural network workloads across multiple machines is a constantly moving target, requiring recalculations for each model and iteration based on availability of resources. Run:AI determines the most efficient and cost-effective way to run a deep learning training workload, taking into account the network bandwidth, compute resources, cost, configurations and the data pipeline and size. We've seen many AI companies in recent years, but Omri, Ronen and Meir's approach blew our mind," she said.
Run:AI's team brings together deep learning, hardware, and parallel computing experts covering different areas of the AI industry, giving them a holistic understanding of the real-world needs of AI development. In stealth since it was founded in 2018, the company has already signed several early customers internationally and has established a US office.
Published by Globes, Israel business news - en.globes.co.il - on April 3, 2019
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