Computation has profoundly shaped the way we approach sensing. In the realm of biosensing, for example, signals are acquired—often at high cost—with various sources of noise, including the stochastic ...

The rising popularity of machine learning and deep learning is by and large due to the massive availability of powerful computing tools and hardware, and the increasing ease of generating and having ...

Federated learning (FL) has emerged as a popular machine learning paradigm which allows multiple data owners to train models collaboratively with out sharing their raw datasets. It holds potential for ...

A Cornell research group led by Prof. Peter McMahon, applied and engineering physics,has successfully trained various physical systems to perform machine learning computations in the same way as a ...

Artificial Deep Neural Networks (DNNs) and, more recently, large-scale foundation models have made breakthrough progress drawing loose structural and ...

Across modern data-intensive disciplines, the union of numerical computation, statistics, and machine learning has become ...

A new technical paper titled “Scaling Deep Learning Computation over the Inter-Core Connected Intelligence Processor” was published by researchers at UIUC and Microsoft Research. “As AI chips ...