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In this paper, we present DevFormer, a novel transformer-based architecture for addressing the complex and computationally demanding problem of hardware design optimization.
Quantifying decoupling capacitor location
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Attention is all you need
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Deep learning for logic optimization algorithms
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Multi-objective optimization of decoupling capacitors for placement and component value
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Effect of power plane inductance on power delivery networks
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Attention, learn to solve routing problems!
Kool, W., van Hoof, H., and Welling, M · 2019
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A Deep Reinforcement Learning Approach for Global Routing
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Vlsi placement parameter optimization using deep reinforcement learning
Agnesina, A., Chang, K., and Lim, S. K · 2020
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Language models are few-shot learners
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Genetic algorithm pdn optimization based on minimum number of decoupling capacitors applied to arbitrary target impedance
de Paulis, F., Cecchetti, R., Olivieri, C., and Buecker, M · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Deep reinforcement learning for analog circuit sizing
Zhao, Z. and Zhang, L · 2020
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Flow network based generative models for non-iterative diverse candidate generation
Bengio, E., Jain, M., Korablyov, M., Precup, D., and Bengio, Y · 2021
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Decision transformer: Reinforcement learning via sequence modeling
Chen, L., Lu, K., Rajeswaran, A., Lee, K., Grover, A., Laskin, M., Abbeel, P., Srinivas, A., and Mordatch, I · 2021
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On joint learning for solving placement and routing in chip design
Cheng, R. and Yan, J · 2021
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Enhancing on-die pdn for optimal use of package pdn with decoupling capacitor
Hwang, J., Pak, J. S., Yoon, D., Lee, H., Jeong, J., Heo, Y., and Kim, I · 2021
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A modified genetic algorithm for the selection of decoupling capacitors in pdn design
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Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al · 2020
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Drills: Deep reinforcement learning for logic synthesis
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Pomo: Policy optimization with multiple optima for reinforcement learning
Kwon, Y.-D., Choo, J., Kim, B., Yoon, I., Gwon, Y., and Min, S · 2020
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Track-assignment detailed routing using attention-based policy model with supervision
Liao, H., Dong, Q., Qi, W., Fallon, E., and Kara, L. B · 2020
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Policy gradient reinforcement learning-based optimal decoupling capacitor design method for 2.5-d/3-d ics using transformer network
Park, H., Kim, M., Kim, S., Jeong, S., Kim, S., Kang, H., Kim, K., Son, K., Park, G., Son, K., Shin, T., and Kim, J · 2020
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Deep reinforcement learning-based optimal decoupling capacitor design method for silicon interposer-based 2.5-d/3-d ics
Park, H., Park, J., Kim, S., Cho, K., Lho, D., Jeong, S., Park, S., Park, G., Sim, B., Kim, S., Kim, Y., and Kim, J · 2020
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An enhanced deep reinforcement learning algorithm for decoupling capacitor selection in power distribution network design
Zhang, L., Huang, W., Juang, J., Lin, H., Tseng, B.-C., and Hwang, C · 2020
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Juang, J., Zhang, L., Kiguradze, Z., Pu, B., Jin, S., and Hwang, C · 2021
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Deep reinforcement learning framework for optimal decoupling capacitor placement on general pdn with an arbitrary probing port
Kim, H., Park, H., Kim, M., Choi, S., Kim, J., Park, J., Kim, S., Kim, S., and Kim, J · 2021
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A graph placement methodology for fast chip design
Mirhoseini, A., Goldie, A., Yazgan, M., Jiang, J., Songhori, E., Wang, S., Lee, Y.-J., Johnson, E., Pathak, O., Nazi, A., Pak, J., Tong, A., Srinivasa, K., Hang, W., Tuncer, E., Le, Q., Laudon, J., Ho, R., Carpenter, R., and Dean, J · 2021
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Jitter-aware economic pdn optimization with a genetic algorithm
Xu, Z., Wang, Z., Sun, Y., Hwang, C., Delingette, H., and Fan, J · 2021
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Do transformers really perform badly for graph representation?
Ying, C., Cai, T., Luo, S., Zheng, S., Ke, G., He, D., Shen, Y., and Liu, T.-Y · 2021
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Transformer network-based reinforcement learning method for power distribution network (pdn) optimization of high bandwidth memory (hbm)
Park, H., Kim, M., Kim, S., Kim, K., Kim, H., Shin, T., Son, K., Sim, B., Kim, S., Jeong, S., Hwang, C., and Kim, J · 2022
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Transformer network-based reinforcement learning method for power distribution network (pdn) optimization of high bandwidth memory (hbm)
Park, H., Kim, M., Kim, S., Kim, K., Kim, H., Shin, T., Son, K., Sim, B., Kim, S., Jeong, S., Hwang, C., and Kim, J · 2022
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