Reinforcement learning (RL) was originally proposed as a framework to allow agents to learn in an online fashion as they interact with their environment. Existing RL algorithms co...
Pascal Poupart, Nikos A. Vlassis, Jesse Hoey, Kevi...
— Negative Bias Temperature Instability (NBTI) in PMOS transistors has become a significant reliability concern in present day digital circuit design. With continued scaling, th...
Sanjay V. Kumar, Chris H. Kim, Sachin S. Sapatneka...
— Many deterministic algorithms in the context of constrained optimization require the first-order derivatives, or the gradient vectors, of the objective and constraint function...
Stephanus Daniel Handoko, Chee Keong Kwoh, Yew-Soo...
In addition to wirelength, modern placers need to consider various constraints such as preplaced blocks and density. We propose a high-quality analytical placement algorithm consi...
While much research has shown that ALNs can produce learning equivalent to FTF classrooms, there has been little empirical research that explicitly and rigorously explores similar...