Recently, a number of classification techniques have been introduced. However, processing
large dataset in a reasonable time has become a major challenge. This made classification …

Abstract Recently, Deep Learning (DL) applications are getting more and more involved in
different fields. Deploying such Deep Neural Networks (DNN) on embedded devices is still a …

The softmax function is widely used in deep neural networks (DNNs), its hardware
performance plays an important role in the training and inference of DNN accelerators …

Long Short-Term Memory (LSTM) networks have been widely used to solve sequence
modeling problems. For researchers, using LSTM networks as the core and combining it …

Secure multi-party computation (MPC) allows a set of parties to jointly compute a function on
their private inputs, and reveals nothing but the output of the function. In the last decade …

Neural Network models most often exploit the SoftMax function in the classification stage for
computing probabilities through exponentiation and division operations. To reduce the …

Today's on-chip systems technology has grounded impressive advances in computing
power and energy consumption. The choice of the right architecture depends on the …

Cameras are widely adopted for high image quality with the rapid advancement of
complementary metal-oxide-semiconductor (CMOS) image sensors while offloading vision …

The astounding development of optical sensing imaging technology, coupled with the
impressive improvements in machine learning algorithms, has increased our ability to …

This paper presents an event-based split-CNN architecture (ESCA) for running time-critical
vision applications with comparatively less memory footprint while consuming low power …