… This article proposes a novel mechanism to reduce the number of circuit topologies to be
sized by utilizing the merits of deep reinforcement learning (RL), which is able to significantly …

… circuit structures to meet the given design constraints. In this work, we propose an automatic
circuit design framework that can generate practical circuit … discuss reinforcement learning …

… in IC design. This work presents a deep reinforcement learning (DRL)-driven approach to IC
… mechanism to effectively address the unique challenges in IC global routing. DRL combines …

… and labeled data. In this work, we propose a data-independent reinforcement learning (RL)
based routing model called Alpha-PD-Router, which learns to route a circuit and …

… Inspired from the great progress in deep reinforcement learning (RL) for AI problems, we
consider building methods that learn to map DFGs onto spatially programmed CGRAs directly …

… circuit to another to reduce the re-design overhead. In this paper, we present GCN-RL Circuit
Designer, leveraging reinforcement learning (… that circuit is a graph, we learn on the circuit …

… We reinforce the fault-prone ECC hardware design by proposing a self-diagnosis ECC fault
detector to achieve fault-tolerance in both the communication channels and the ECC circuitry…

… LEARNING Having outlined the global research achievements in the area of electronic
circuit design and application of RL and deep RL through the bibliographic method, this section …

… Reinforcement learning (RL) has gained attention recently as an optimization algorithm for
chip design… solve logic synthesis for structured datapath blocks such as prefix circuits. Several …

… learning process. This work proposes ESE, a reinforcement learning based framework to
eiciently learn … (ML) open up new opportunities for integrated circuit design methods. Deploying …