Constructive deep reinforcement learning (DRL)-based hybrid equalizer design for next generation high bandwidth memory (HBM)차세대 고대역폭 메모리의 하이브리드 이퀄라이저 설계를 위한 건설적 강화학습 방법론
In this thesis, we proposed constructive deep reinforcement learning (DRL)-based hybrid equalizer design
for next generation high bandwidth memory (HBM). The hybrid equalizer was configured with a passive
equalizer as high-pass-filter and active equalizer for high frequency boosting. We used the constructive
reinforcement learning for optimization of hybrid equalizer parameters, and the neural network was
trained to suggest the optimal hybrid equalizer design for an arbitrary channel dimension. By using
domain knowledge about the equalizer circuit, we set the range of parameters that can be designed.
Therefore, we improved the optimization and learning efficiency of training by excluding the meaningless
data from learning. As well as, we reduced the time cost of reward extraction through channel simulation
modeling. For verification of trained neural network, we compared the proposed methodology with
conventional optimization method such as genetic algorithm and random search in terms of performance.
As a result, we proved that proposed method has optimality and time efficiency.