The attention mechanism is becoming increasingly popular in Natural Language Processing
(NLP) applications, showing superior performance than convolutional and recurrent …

Today's applications generate a large amount of data that need to be processed by learning
algorithms. In practice, the majority of the data are not associated with any labels …

Deep Learning (DL) systems have demonstrated unparalleled performance in many
challenging engineering applications. As the complexity of these systems inevitably …

Neural network (NN) accelerators have been integrated into a wide-spectrum of computer
systems to accommodate the rapidly growing demands for artificial intelligence (AI) and …

Today's systems rely on sending all the data to the cloud and then using complex
algorithms, such as Deep Neural Networks, which require billions of parameters and many …

The capabilities of artificial neural networks are rapidly evolving, so are the expectations for
them to solve ever more challenging tasks in numerous everyday situations. Larger, more …

Data-intensive workloads and applications, such as machine learning (ML), are
fundamentally limited by traditional computing systems based on the von-Neumann …

Associative Processors (AP) enable in-situ, data-parallel computation in content-
addressable memories (CAM). In particular, arithmetic operations are accomplished via bit …

Today's systems, especially in the age of federated learning, rely on sending all the data to
the cloud, and then use complex algorithms, such as Deep Neural Networks, which require …

Processing Using Memory (PUM) accelerators have the potential to perform Deep Neural
Network (DNN) inference by using arrays of memory cells as computation engines. Among …