The next-generation of wireless networks will enable many machine learning (ML) tools and
applications to efficiently analyze various types of data collected by edge devices for …

In recent years, deep learning has garnered tremendous success in a variety of application
domains. This new field of machine learning has been growing rapidly and has been …

Large language models have been widely adopted but require significant GPU memory for
inference. We develop a procedure for Int8 matrix multiplication for feed-forward and …

Communication systems to date primarily aim at reliably communicating bit sequences.
Such an approach provides efficient engineering designs that are agnostic to the meanings …

This chapter provides approaches to the problem of quantizing the numerical values in deep
Neural Network computations, covering the advantages/disadvantages of current methods …

Deep neural networks have been applied in many applications exhibiting extraordinary
abilities in the field of computer vision. However, complex network architectures challenge …

Cross-silo federated learning (FL) enables organizations (eg, financial, or medical) to
collaboratively train a machine learning model by aggregating local gradient updates from …

Stateful optimizers maintain gradient statistics over time, eg, the exponentially smoothed
sum (SGD with momentum) or squared sum (Adam) of past gradient values. This state can …

In recent years, machine learning (ML) and deep learning (DL) have shown remarkable
improvement in computer vision, natural language processing, stock prediction, forecasting …

Spiking neural networks (SNNs) are inspired by information processing in biology, where
sparse and asynchronous binary signals are communicated and processed in a massively …