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Design For Test Data

Creating chips that can be tested throughout their expected lifetimes.

As design pushes deeper into data-driven architectures, so does test. Geir Eide, director for product management of DFT and Tessent Silicon Lifecycle Solutions at Siemens Digital Industries Software, talks with Semiconductor Engineering about a subtle but significant shift for designing testability into chips so that test data can be used at multiple stages during a device’s lifetime.

VMware and NVIDIA announce Private AI Foundation

The new VMware Private AI Foundation With NVIDIA enables enterprises to prepare their Businesses for Generative AI.

Speaking at the opening keynote of VMware’s Explore conference in Las Vegas today, Vmware CEO Raghu Raghuram and NVIDIA’s CEO Jensen Huang jointly announced the expansion of their strategic partnership to ready the hundreds of thousands of enterprises that run on VMware’s cloud infrastructure for the era of generative AI.

“Generative AI and multi-cloud are the perfect match,” said Raghu Raghuram, CEO, VMware. “Customer data is everywhere — in their data centers, at the edge, and in their clouds. Together with NVIDIA, we’ll empower enterprises to run their generative AI workloads adjacent to their data with confidence while addressing their corporate data privacy, security and control concerns.”

“Enterprises everywhere are racing to integrate generative AI into their businesses,” Jensen Huang, founder and CEO, NVIDIA added. “Our expanded collaboration with VMware will offer hundreds of thousands of customers — across financial services, healthcare, manufacturing and more — the full-stack software and computing they need to unlock the potential of generative AI using custom applications built with their own data.”

McKinsey estimates that generative AI could add up to $4.4 trillion annually to the global economy. 

VMware Private AI Foundation with NVIDIA will enable enterprises to harness this capability, customizing large language models; producing more secure and private models for their internal usage; and offering generative AI as a service to their users; and, more securely running inference workloads at scale.

The platform is expected to include integrated AI tools to empower enterprises to run proven models trained on their private data in a cost-efficient manner. To be built on VMware Cloud Foundation and NVIDIA AI Enterprise software, according to the two companies, the platform’s expected benefits will include:

• Privacy — Will enable customers to easily run AI services adjacent to wherever they have data with an architecture that preserves data privacy and enable secure access.

• Choice — Enterprises will have a wide choice in where to build and run their models — from NVIDIA NeMo™ to Llama 2 and beyond — including leading OEM hardware configurations and, in the future, on public cloud and service provider offerings.

• Performance — Running on NVIDIA accelerated infrastructure will deliver performance equal to and even exceeding bare metal in some use cases, as proven in recent industry benchmarks.

• Data-Center Scale — GPU scaling optimizations in virtualized environments will enable AI workloads to scale across up to 16 vGPUs/GPUs in a single virtual machine and across multiple nodes to speed generative AI model fine-tuning and deployment.

• Lower Cost — Will maximize usage of all compute resources across, GPUs, DPUs and CPUs to lower overall costs, and create a pooled resource environment that can be shared efficiently across teams.• Accelerated Storage — VMware vSAN Express Storage Architecture will provide performance-optimized NVMe storage and supports GPUDirect® storage over RDMA, allowing for direct I/O transfer from storage to GPUs without CPU involvement.

• Accelerated Networking — Deep integration between vSphere and NVIDIA NVSwitch™ technology will further enable multi-GPU models to execute without inter-GPU bottlenecks.

• Rapid Deployment and Time to Value — vSphere Deep Learning VM images and image repository will enable fast prototyping capabilities by offering a stable turnkey solution image that includes frameworks and performance-optimized libraries pre-installed.

VMware Private AI Foundation with NVIDIA will be supported by Dell Technologies, Hewlett Packard Enterprise and Lenovo — which will be among the first to offer systems that supercharge enterprise LLM customization and inference workloads with NVIDIA L40S GPUs, NVIDIA BlueField®-3 DPUs and NVIDIA ConnectX®-7 SmartNICs.

The NVIDIA L40S GPU enables up to 1.2x more generative AI inference performance and up to 1.7x more training performance compared with the NVIDIA A100 Tensor Core GPU.

Also announced was the availability of fully tested reference architectures with either two, four or eight GPU models ready for customer test projects.

It would still seem however that most business applications for this kind of AI are little more than souped-up ‘help desk’ systems where the AI is used to distil and extract from existing knowledge bases within the organisation.

At an event pre-briefing, we asked about this and an NVIDIA representative said, “I think that there's all of these use cases across industries and the possibilities are pretty endless. I'll be honest, like when we look at this, we're looking at generative AI to help software developers write code. We're looking at generative AI to help security teams almost write policy as code where they don't need to understand the low level programming of regex and other languages. We're using it as co-pilots to help people ask questions in the business systems. So I think, the uses will range across industry, and there's quite a broad number that we're going to see coming out as this new technology and new research is being applied to businesses in practice.”

Unfortunately, that still sounds a lot like ‘help desk’ to us. Perhaps we well see new and exciting uses, but not for a while.

The author is attending VMware Explore as a guest of VMware.

VMware Inc.

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