AI in EE

AI IN DIVISIONS

AI in Wave Division

AI in EE

AI IN DIVISIONS

AI in Wave Division

AI in Wave Division

Sequential Policy Network-based Optimal Passive Equalizer Design for an Arbitrary Channel of High Bandwidth Memory using Advantage Actor Critic (EPEPS 2021)

Title:

 

Sequential Policy Network-based Optimal Passive Equalizer Design for an Arbitrary Channel of High Bandwidth Memory using Advantage Actor Critic (EPEPS 2021)

 

Authors:

 

Seonguk Choi, Minsu Kim, Hyunwook Park, Keeyoung Son, Seongguk Kim, Jihun Kim, Joonsang Park, Haeyeon Kim, Taein Shin, Keunwoo Kim and Joungho Kim.

 

Abstract:

In this paper, we proposed a sequential policy network-based passive equalizer (PEQ) design method for an arbitrary channel of high bandwidth memory (HBM) using advantage actor critic (A2C) algorithm, considering signal integrity (SI) for the first time. PEQ design must consider the circuit parameters and placement for improving the performance. However, optimizing PEQ is complicated because various design parameters are coupled. Conventional optimization methods such as genetic algorithm (GA) repeat the optimization process for the changed conditions. In contrast, the proposed method suggests the improved solution based on the trained sequential policy network with flexibility for unseen conditions. For verification, we conducted electromagnetic (EM) simulation with optimized PEQs by GA, random search (RS) and the proposed method. Experimental results demonstrate that the proposed method outperformed the GA and RS by 4.4 \% and 6.4 \% respectively in terms of the eye-height.