New Chip Design to Revolutionize AI in Portable Devices
The hardware bottleneck in AI and data science: the need for better memory capacity As AI and data science applications continue to advance, the size of neural networks required to process data is doubling at a rapid pace. However, the hardware capability needed to support these networks is not keeping up with the demand. This has created a significant bottleneck in the field, as software is limited by the hardware on which it runs. The problem is especially acute in portable devices, which require smaller and more energy-efficient chips to support heavy computation. While traditional silicon chips have been the primary hardware solution for decades, their limitations have become increasingly evident as the demands of AI and data science applications have grown. The need for better memory capacity is a key driver of this bottleneck. Memory is crucial for storing and processing large amounts of data, and traditional silicon chips are limited in their ability to sup...