Innoviz Selects Weka to Accelerate its Artificial Intelligence (AI) and Deep Learning Workflows
Innoviz, a manufacturer of Solid-state Light Detection and Ranging (LiDAR) sensors and Perception Software, has selected the Weka File System (WekaFS) to accelerate its Artificial Intelligence (AI) and deep learning workflows. WekaFS, a product of WekaIO, a company the works in the field of file storage for data-intensive applications, will improve application performance at scale and deliver high bandwidth I/O to its GPU cluster.
Innoviz’s solid-state LiDAR sensors are key to the future of autonomous cars. The sensors and Perception Software, which identifies, classifies, segments, and tracks objects to give autonomous vehicles a better understanding of the 3D driving scene, rely heavily on AI. Having recently closed its Series C funding round with $170M secured, Innoviz is choosing and developing the right technologies to empower it to realize its expansion plans and enhance its manufacturing capabilities. Part of its vision is to accelerate the progress to mass production and to meet the growing demand for affordable sensing solutions that enable autonomy. Innoviz selected WekaFS because performance improvements with WekaFS matched the company’s needs.
According to the company, WekaFS was architected to leverage the benefits of NVMe flash technology, delivering high-performance, high-bandwidth, and low-latency storage infrastructure to meet the demands of today’s GPU-enabled AI and High-Performance Computing (HPC) workloads in the data center and in the cloud. The company further claims that WekaFS is the world’s fastest and most scalable file system for AI systems, which generate unpredictable I/O workloads with highly random-access patterns across both small and large files.
“Weka’s storage scalability and ability to grow the infrastructure without losing performance, was a key factor in the decision to select the Weka file system,” said Oren Ben Ibghei, IT manager, Innoviz.
“Customers who are like Innoviz in terms of managing multiple petabytes of data on-premises and supporting I/O-intensive workloads are looking for alternatives to legacy storage systems that are costly at scale and deliver sub-optimal performance,” said Doron Zuberman, executive vice president, E&M Computing (Emet), the integrator that delivered the solution in partnership with Weka. “The larger the dataset, the better the AI outcomes, so immediate access to data lakes is a critical requirement for a storage solution. Being a software-only solution, Weka is the perfect storage alternative for AI workloads as it allows for the most economic build-out of infrastructure at scale.”
Source: Press Release
