April 22, 2023
Esperanto Technologies is working to accelerate software support for its novel, high-performance AI chips. The company has ported a large-language model (LLM) f Read more…
December 16, 2022
The European Union will release €270 million in funds as it tries to attain technology independence by building chips based on the open RISC-V instruction set Read more…
December 13, 2022
The ability to spin up custom chip designs at a lower cost has made the RISC-V architecture attractive for devices that don't require cutting edge chips. Ventana Micro Systems is now bringing that ability to RISC-V chips for servers, with plans to release chips for high-performance computers in the future. Ventana said... Read more…
November 18, 2022
One of the original RISC-V designers this week boldly predicted that the open architecture will surpass rival chip architectures in performance. "The prediction is two or three years we'll be surpassing your architectures and available performance with... Read more…
November 16, 2022
Europe’s sovereign approach to exascale computing is complicating plans for U.S. chipmakers to breakthrough in the market, and in the process, empowering local chipmakers. For one, a European chip startup called SiPearl is emerging as an early... Read more…
September 23, 2022
Nvidia is not interested in bringing software support to its GPUs for the RISC-V architecture despite being an early adopter of the open-source technology in its GPU controllers. Nvidia has no plans to add RISC-V support for CUDA, which is the proprietary GPU software platform, a company representative... Read more…
August 25, 2022
Some chip pioneers from the 1980s are raising the ante in modern chip design with new opportunities provided by artificial intelligence and the open-source RISC-V architecture. Untether AI, which was co-founded by an analog and mixed signal chip pioneer Martin Snelgrove, released a new AI inferencing chip called Boqueria, which has more than 1,400 optimized RISC-V processors. That chip will compete... Read more…
August 11, 2022
A Google-led program to design and manufacture chips for free is becoming popular among researchers and computer enthusiasts. The search giant's open silicon program is providing the tools for anyone to design chips, which then get manufactured. Google foots the entire bill, from a chip's conception to delivery of the final product in a user's hand. Google's... 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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