The omnipresence of NP-hard combinatorial optimization problems (COPs) compels domain
experts to engage in trial-and-error heuristic design. The long-standing endeavor of design …

In this article, for the first time, we propose a transformer network-based reinforcement
learning (RL) method for power distribution network (PDN) optimization of high bandwidth …

In this paper, we present DevFormer, a novel transformer-based architecture for addressing
the complex and computationally demanding problem of hardware design optimization …

This paper proposes an advantage actor–critic (A2C) reinforcement learning (RL)–based
method for the optimization of decoupling capacitor (decap) design. Unlike the previous RL …

In this paper, for the first time, we model and analyze the impacts of parallel I/O interface
factors on system-level power supply noise induced jitter (PSIJ) in 4 Gbps HBM. PSIJ is …

This paper proposes a deep reinforcement learning (DRL) framework that learns a reusable
policy to find the optimal placement of decoupling capacitors (decaps) on power distribution …

In this paper, we first propose a scalable transformer network-based reinforcement learning
(RL) method for power supply induced jitter (PSIJ) optimization in high bandwidth memory …

This paper proposes a novel problem-solving strategy for simultaneous escape routing
(SER) by imitation learning (IL) which is beneficial to exclude human experts' domain …

In this paper, for the first time, we propose an explainable reinforcement learning (XRL)-
based decap placement optimization method for high bandwidth memory (HBM) considering …

In this paper, we propose SI/PI co-design method using Bayesian optimization (BO) for
power supply noise induced jitter (PSIJ) reduction in 12.8 Gbps high bandwidth memory …