Using AI to solve mission-critical problems in the aerospace and defense industry is becoming more of a reality. New technologies emerge every day that can simplify deployment, management, and scaling of AI infrastructure to ensure long-term ROI.

There are several questions to ask yourself to ensure deploying AI workloads, and harnessing the full potential of data, in aerospace/defense is much more plausible and efficient.

What are the demands AI workloads place on hardware?

AI workloads have ushered in a new hardware standard that relies heavily on GPU acceleration. AI has also changed what performance storage looks like. The need to feed data to GPUs has completely bottlenecked legacy network fabrics and storage architectures. It is best practice to keep highly utilized hardware on-prem, and to keep the storage next to the primary compute resource. This both improves performance and reduces TCO when compared to public cloud offerings.

One of the tools we use to overcome AI hardware demands and maintain flexibility is composable infrastructure. At its core, CDI is a set of disaggregated resources connected by a PCIe-based fabric that enables you to dynamically provision bare metal instances via a GUI. It uses cloud native design principles to deliver best-in-class performance and flexibility. This gives you the flexibility of cloud, and the value of virtualization, with the performance of bare metal. Composite infrastructure also works with a range of security options, so you can secure your workloads by leveraging at rest and in-flight encryption, implement a zero-trust network, run MLS, etc.

What are the new CPU technologies that can improve ROI?

In order to keep pace with the requirements of AI workloads, hardware is evolving. Both AMD and Intel are shipping CPUs that support PCIe Gen 4 with support for up to 32GB/s of bandwidth per 16-lane slot. This is primarily exploited by GPUs, accelerators, and high bandwidth network adapters.

AMD Milan and Intel Ice Lake processors both support 4TB of memory per socket. Silicon Mechanics’ design solutions are based on modern CPU platforms paired with technologies like CDI and Weka storage. These performance improvements increase ROI by reducing time-to-value.

What are the advantages of GPU-accelerated training and inference?

Running the inference part of your neural network can be both challenging and time-consuming. Saving time during processing helps deliver a better application experience. CPUs no longer provide the performance necessary to handle inference workloads.

New AI hardware standards rely heavily on GPU acceleration. GPU-accelerated training and inference is now advantageous. The latest version of NVIDIA’s inference server software, the Triton 2.3, and the Ampere architecture in the A100 GPU make it easier, faster, and more efficient to use GPU acceleration.

These GPU platforms provide unmatched performance, seamless scalability, and the kind of flexibility not found in out-of-the-box solutions.

How can you remove storage bottlenecks?

The aerospace/defense industry compiles mountains of data through research, data modeling, and neural network training. Ingest and distribution of all this data can present its own set of challenges.

GPU direct storage allows for a straight path to local or remote storage like NvME_oF directly to GPU memory. These kinds of innovations, along with high-speed network and storage connections, can enable a multitude of storage options including NvME_oF, RDMA over converged ethernet, Weka storage and almost anything that is available today. We are using these technologies to help remove roadblocks to allow you to realize your GPU accelerated goals.

To learn more about defense computing and military AI, watch our on-demand webinar at https://sourcecode.ac-page.com/silicon-mechanics-harnessing-the-aerospacedefense-data-explosion.