Blog: AI's Memory Problem

Find the Latest SoC Solutions
for... Automotive... IoT... Security... Audio... Video

Artificial intelligence, which allows computers to learn and perform specific tasks such as image recognition and natural language processing, is loosely inspired by the human brain.

eetasia.com, Aug. 27, 2019 – 

Artificial Intelligence (AI), which allows computers to learn and perform specific tasks such as image recognition and natural language processing, is loosely inspired by the human brain.

The challenge is that while the human brain has evolved over the last 3 million years, Artificial Neural Networks, the very "brain" of AI, which have only been around for a few decades and aren't nearly as finely tuned or as sophisticated as the gray matter in our heads, are expected to perform tasks associated with human intelligence. So in our quest to create AI systems that can benefit society – in areas from image classification to voice recognition and autonomous driving – we need to find new paths to speed up the evolution of AI.

Part of this process is figuring out what types of memory work best for specific AI functions and discovering the best ways to integrate various memory solutions together. From this standpoint, AI faces two main memory limitations: density and power efficiency. AI's need for power makes it difficult to scale AI outside of datacenters where power is readily available – particularly to the edge of the cloud where AI applications have the highest potential and value.

To enable AI at the edge, developments are being made toward domain-specific architectures that facilitate energy-efficient hardware. However, the area that will open the way for the most dramatic improvements, and where a large amount of effort should be concentrated, is the memory technology itself.

Click here to read more...

 Back