Addressing Design Flow Gaps and Creating Generic AI Solutions

The gap between what the best AI applications can perform today versus the human brain is vast. Among many other differences, power efficiency and learning speed are two of the most challenging factors the AI & ML industry is dealing with when trying to design brain-like neural networks.

Today, in the final episode of the series, Mike and Ellie discuss that gap and the challenges that hardware designers have in their design flow. They also touch on the clashing requirements of coming up with a generic AI application that can perform many tasks versus applications that perform one task really well.

Tune in, to find out what the AI industry is doing to narrow the gap between the brain and artificial intelligence.

In this episode, you will learn:

• The gaps between AI applications and the human brain. (00:45)
• The Holy Grail of AI: one-shot learning. (01:48)
• The energy consumption of the human brain versus deep neural networks. (02:50)
• The industry’s struggle of creating specific networks versus generic ones. (03:56)
• The resources required by one of the most complex neural networks. (06:08)
• The industry’s challenge of keeping up with the rapid changes in AI architectures. (06:57)

Connect with Mike Fingeroff:

• LinkedIn

Connect with Ellie Burns:

• LinkedIn

Resources:

• Catapult High-Level Synthesis
• Siemens EDA