This paper presents a robust deep in-memory machine learning classifier with a stochastic
gradient descent (SGD)-based on-chip trainer using a standard 16-kB 6T SRAM array. The …

The acclaimed successes of neural networks often overshadow their tremendous
complexity. We focus on numerical precision–a key parameter defining the complexity of …

We address the design of inference-oriented communication systems where multiple
transmitters send partial inference values through noisy communication channels, and the …

In recent years, researchers have attempted to explore methods for real-time image forgery
detection. Many approaches were developed to detect a certain number of image …

The current researches are primarily focused on optimizing the available classification
methods in support vector machines (SVMs). The basic idea of this article to highlight the …

The concept of deep in-memory architecture (DIMA) described in this book is based on the
doctoral dissertation research of the first two coauthors conducted under the supervision of …

Facial activity is the most powerful and natural means for understanding emotional
expression for humans. Recent years, extensive efforts have been devoted to facial …

Abstract Machine learning has become an integral part of everyday life ranging from
applications in AI-powered search queries to (partial) autonomous driving. Many of the …

In this article, a discriminative fast hierarchical learning algorithm is developed for
supporting multiclass image classification, where a visual tree is seamlessly integrated with …

We present a principled framework to address resource allocation for realizing boosting
algorithms on substrates with communication noise. Boosting classifiers (eg, AdaBoost) …