A full version of this article was first published on Coruzant Technologies
From self-driving cars to cashier-less stores, deep learning is set to transform industries, generate growth opportunities, and deliver benefits to society. However, the processes that come with it are complex and expensive.
“Latency and energy consumption are closely linked and are influenced by the overall architectural complexity of the deep learning model itself.” The more organizations strive to reduce latency, the more compute power and energy is needed—and this raises costs.
For deep learning to produce substantial value, there should be a balance between increasing inference and minimizing latency, while maintaining accuracy. Accelerators that can optimize models for any hardware and tasks are making this possible.
I shared more about this on Coruzant Technologies. Click here to read the article and learn more about the challenges of deep learning and how to achieve acceleration as a viable solution.