First-order derivatives: n additional function calls are needed. Second-order derivatives based on gradient calls, when the "grd" module is specified (Dennis and Schnabel 1983): n additional gradient ...

Alzheimer's disease (AD) is a highly heritable disorder, the genetic underpinnings of which remain incompletely understood despite intense research over the past 30 years. Over 1,000 individual ...

Abstract: Approximate multipliers (AppMults) are widely employed in deep neural network (DNN) accelerators to reduce the area, delay, and power consumption. However, the inaccuracies of AppMults ...

Abstract: The inverse dynamics of the six degree-of-freedom (6-DOF) parallel robot (PR) presents an inherent complexity due to the closed-loop kinematic chains. To derive computational efficient ...

Abstract: The successive convex approximation (SCA) methods stand out as the viable option for nonlinear optimization-based control, as it effectively addresses the challenges posed by nonlinear ...

Abstract: Convolutional neural networks (CNNs), despite their broad applications, are constrained by high computational and memory requirements. Existing compression techniques often neglect ...

Abstract: With the development of computation methods and the requirement of data processing, it is often required to execute electromagnetic (EM) simulations in a lot of different complex formation ...

Abstract: The sigmoid function is a representative activation function in shallow neural networks. Its hardware realization is challenging due to the complex exponential and reciprocal operations.

Abstract: Experimental development of gate-all-around silicon nanowire field-effect transistors (NWFETs), a viable replacement for FinFETs, can be complemented by technology computer-aided design.

Abstract: Transformer-based neural networks (NNs) prevail in today’s artificial intelligence applications, including autonomous driving, natural language processing and generative modeling, showing ...