In this work, we formulate the decision problem for reversible operators with training time as the objective function and memory usage as the constraint.
With the recent advances in optical phase change material (PCM), photonic in-memory neurocomputing has demonstrated its superiority in optical neural network (ONN) designs with near-zero static power consumption, time-of-light latency, and compact footprint.
In this work, we propose a closed-loop ONN on-chip learning framework L2ight to enable scalable ONN mapping and efficient in-situ learning.
In physical design, human designers typically place macros via trial and error, which is a Markov decision process.
1 code implementation • 4 Dec 2020 • Shubham Rai, Walter Lau Neto, Yukio Miyasaka, Xinpei Zhang, Mingfei Yu, Qingyang Yi Masahiro Fujita, Guilherme B. Manske, Matheus F. Pontes, Leomar S. da Rosa Junior, Marilton S. de Aguiar, Paulo F. Butzen, Po-Chun Chien, Yu-Shan Huang, Hoa-Ren Wang, Jie-Hong R. Jiang, Jiaqi Gu, Zheng Zhao, Zixuan Jiang, David Z. Pan, Brunno A. de Abreu, Isac de Souza Campos, Augusto Berndt, Cristina Meinhardt, Jonata T. Carvalho, Mateus Grellert, Sergio Bampi, Aditya Lohana, Akash Kumar, Wei Zeng, Azadeh Davoodi, Rasit O. Topaloglu, Yuan Zhou, Jordan Dotzel, Yichi Zhang, Hanyu Wang, Zhiru Zhang, Valerio Tenace, Pierre-Emmanuel Gaillardon, Alan Mishchenko, Satrajit Chatterjee
If the function is incompletely-specified, the implementation has to be true only on the care set.