  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 (DNN) using 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 40nm memristor
macro, on ResNet and PointNet++ for classifying images and 3D points from the
MNIST and ModelNet datasets, which not only achieves accuracy on par with
software but also a 48.1% and 15.9% reduction in computational budget.
Moreover, it delivers a 77.6% and 93.3% reduction in energy consumption.
