Optimizing Heterogeneous Computing at Edge AI
A 2-Minute Video on Heterogeneous Computing and Edge AI Applications
  • Why heterogeneous computing is the best strategy for deploying artificial intelligence at the edge
  • How to optimize an AI platform with CPU, GPU, FPGA, and ASIC
  • The benefits of bringing AI to the edge, including faster response time, enhanced security, improved mobility, and lower communication costs
WATCH VIDEO

Optimizing Heterogeneous Computing at Edge AI

Performing AI workloads at the edge alleviates cost, security, and latency concerns associated with transferring data to the cloud for processing. However, determining IT infrastructure for edge AI applications can be a costly, time-consuming and laborious experimental process. It is unsurprising that implementation is the most-cited challenge by early AI adopters, according to Deloitte State of AI in the Enterprise, 2nd Edition. Learn how to maximize the innovation and productivity gains from AI with ease.

In this Video, you will learn:

  • Why heterogeneous computing is the best strategy for deploying artificial intelligence at the edge
  • How to optimize an AI platform with CPU, GPU, FPGA, and ASIC
  • The benefits of bringing AI to the edge, including faster response time, enhanced security, improved mobility, and lower communication costs

Watch A 2-Minute Video

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