The development of artificial intelligence systems is creating an opportunity for new custom and semicustom semiconductor devices. Technology strategic planners at semiconductor vendors must put in place the partnerships and toolsets to leverage this opportunity.
Impacts and Recommendations
- The efficient training of neural network models will require high-bandwidth, high throughput parallel processing, often above and beyond the capabilities of today's classic von Neumann architecture-based computers
- The use of simpler processing devices in the deployment of a trained neural network for inferencing will enable flexibility as to where the AI solution is deployed
- Data Center or Cloud Deployments
- At the Network Edge
- AI algorithms are still relatively immature and evolving, which will drive a need for flexible device designs and fast prototyping capabilities
- Achieving business success with any AI-specific semiconductors will require the availability of a comprehensive set of software development tools and support for all the relevant AI software frameworks
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