Reconfigurable heterogeneous integration using stackable chips with embedded artificial intelligence

Abstract

Artificial intelligence applications have changed the landscape of computer design, driving a search for hardware architecture that can efficiently process large amounts of data. Three-dimensional heterogeneous integration with advanced packaging technologies could be used to improve data bandwidth among sensors, memory and processors. However, such systems are limited by a lack of hardware reconfigurability and the use of conventional von Neumann architectures. Here we report stackable hetero-integrated chips that use optoelectronic device arrays for chip-to-chip communication and neuromorphic cores based on memristor crossbar arrays for highly parallel data processing. With this approach, we create a system with stackable and replaceable chips that can directly classify information from a light-based image source. We also modify this system by inserting a preprogrammed neuromorphic denoising layer that improves the classification performance in a noisy environment. Our reconfigurable three-dimensional hetero-integrated technology can be used to vertically stack a diverse range of functional layers and could provide energy-efficient sensor computing systems for edge computing applications. By using optoelectronic device arrays for chip-to-chip communication and neuromorphic cores based on memristor crossbar arrays for highly parallel data processing, reconfigurable and stackable hetero-integrated chips can be created for use in edge computing applications.