From creating 3D models for industrial design and prototyping to using visualization and simulation for surgical planning, graphics are used to visualize and analyze data in new and innovative ways. With the integration of artificial intelligence (AI) and machine learning (ML), these graphics are becoming even more powerful and enabling more accurate decisions and improved outcomes.
When faced with complex design challenges that come along with medical and industrial computing, design engineers and managers have choices to make. They can bring all the expertise and know-how in-house to perform the entire design, prototype, and test, or they can use an original equipment manufacturer (OEM) and integrate technology from market leaders. Both approaches have merit depending on the cost and time-to-market constraints.
If manufacturing volumes are very high and time to market is not a critical constraint, then every penny counts, and in-house design and expertise will ultimately be the best choice. But high-end, low-volume applications benefit most by integrating leading-edge technology into products or designs.
Industrial Computing-Specific Challenges
The performance of high-end applications, such as mission-critical medical machines, industrial manufacturing, and harsh environments, can be impacted by real-time demands and the limitations of having a single central processor. To overcome this, partitioning a design and using pipelining techniques is often necessary.
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