Semantic memory-based dynamic neural network using memristive ternary CIM and CAM for 2D and 3D vision.

ABSTRACT: The brain is dynamic, associative, and efficient. It reconfigures by associating the inputs with past experiences, with fused memory and processing. In contrast, AI models are static, unable to associate inputs with past experiences, and run on digital computers with physically separated memory and processing. We propose a hardware-software co-design, a semantic memory-based dynamic neural network using a memristor. The network associates incoming data with the past experience stored as semantic vectors. The network and the semantic memory are physically implemented on noise-robust ternary memristor-based computing-in-memory (CIM) and content-addressable memory (CAM) circuits, respectively. We validate our co-designs, using a 40-nm memristor macro, on ResNet and PointNet++ for classifying

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC11323881 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature