July 28, 2022
Scientists have uncovered a new type of quantum cryptography that utilizes one of the same laws of physics used in building quantum computers: quantum entanglement. Quantum entanglement, or what Einstein called “spooky action at a distance,” refers to the phenomenon of two subatomic particles being linked to one another in an exclusive... Read more…
August 9, 2021
Researchers from the Technical University of Munich (TUM) have designed and commissioned fabrication of chip intended to implement so-called post-quantum crypto Read more…
April 9, 2020
A coordinated, long-term approach is needed to confront the “retroactive risk” to secure communications posed by quantum computing, warns a new report empha Read more…
August 15, 2016
Within the next few days China will launch a ‘quantum’ communications satellite heralding yet another major technology achievement for the world’s largest Read more…
January 23, 2012
Blind quantum computing protocol preserves the privacy of user data in the cloud. Read more…
The increasing complexity of electric vehicles result in large and complex computational models for simulations that demand enormous compute resources. On-premises high-performance computing (HPC) clusters and computer-aided engineering (CAE) tools are commonly used but some limitations occur when the models are too big or when multiple iterations need to be done in a very short term, leading to a lack of available compute resources. In this hybrid approach, cloud computing offers a flexible and cost-effective alternative, allowing engineers to utilize the latest hardware and software on-demand. Ansys Gateway powered by AWS, a cloud-based simulation software platform, drives efficiencies in automotive engineering simulations. Complete Ansys simulation and CAE/CAD developments can be managed in the cloud with access to AWS’s latest hardware instances, providing significant runtime acceleration.
Two recent studies show how Ansys Gateway powered by AWS can balance run times and costs, making it a compelling solution for automotive development.
Five Recommendations to Optimize Data Pipelines
When building AI systems at scale, managing the flow of data can make or break a business. The various stages of the AI data pipeline pose unique challenges that can disrupt or misdirect the flow of data, ultimately impacting the effectiveness of AI storage and systems.
With so many applications and diverse requirements for data types, management systems, workloads, and compliance regulations, these challenges are only amplified. Without a clear, continuous flow of data throughout the AI data lifecycle, AI models can perform poorly or even dangerously.
To ensure your AI systems are optimized, follow these five essential steps to eliminate bottlenecks and maximize efficiency.
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