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.

翻译：大脑具有动态性、联想性与高效性。它通过将输入与过往经验相关联进行重构，实现了记忆与处理的融合。相比之下，人工智能模型是静态的，无法将输入与过往经验关联，且在物理上分离存储与处理的数字计算机上运行。我们提出一种软硬件协同设计——基于语义记忆的动态神经网络（DNN），该网络利用忆阻器实现。该网络能够将输入数据与存储为语义向量的过往经验进行关联。网络与语义记忆分别通过基于抗噪声三值忆阻器的存内计算（CIM）电路与内容可寻址存储器（CAM）电路进行物理实现。我们使用40纳米忆阻器宏单元，在ResNet与PointNet++网络上对MNIST和ModelNet数据集中的图像与三维点进行分类验证。该设计不仅达到了与软件相当的精度，同时将计算开销降低了48.1%与15.9%，能耗更降低了77.6%与93.3%。