This presentation details a fully programmable, associative, content-based, compute in-memory architecture that changes the concept of computing from serial data processing—where data is moved back and forth between the processor and memory—to massive parallel data processing, compute, and search directly in-place.

 

This associative processing unit (APU) can be used in many machine learning applications, one-shot/few-shot learning, convolutional neural networks, recommender systems and data mining tasks such as prediction, classification, and clustering.

 

Additionally, the architecture is well-suited to processing large corpora and can be applied to Question Answering (QA) and various NLP tasks such as language translation. The architecture can embed long documents and compute in-place any type of memory network and answer complex questions in O(1).