https://ipsj.ixsq.nii.ac.jp/records/229345

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aitemu

 1. shinpoziumu
 2. shinpoziumushirizu
 3. gemupuroguraminguwakushiyotsupu(GPWS)
 4. 2023

Playing Board Games with a Deep Convolutional Neural Network on the
Motorola 6809 8-Bit Microprocessor

https://ipsj.ixsq.nii.ac.jp/records/229345
https://ipsj.ixsq.nii.ac.jp/records/229345
9e1431e2-99f7-4d16-8f82-d7dcfa6a2045

     Ming Qian  / huairu                 raisensu           akushiyon
IPSJ-GPWS2023012.pdf       Copyright (c) 2023 by the      daunro
IPSJ-GPWS2023012.pdf       Information Processing Society do 
(275.3 kB)                 of Japan                       Information
opunakusesu


  Item   Symposium(1)
  type
 Gong Kai Ri   2023-11-10
taitoru
 taito  Playing Board Games with a Deep Convolutional Neural Network
   ru    on the Motorola 6809 8-Bit Microprocessor
taitoru
  Yan Yu    en
 taito  Playing Board Games with a Deep Convolutional Neural Network
   ru    on the Motorola 6809 8-Bit Microprocessor
  Yan Yu 
  Yan Yu    eng
kiwa
   do
  Zhu Ti    Other
 Scheme
  Zhu Ti    deep learning
kiwa
   do
  Zhu Ti    Other
 Scheme
  Zhu Ti    quantization
kiwa
   do
  Zhu Ti    Other
 Scheme
  Zhu Ti    neural networks
Zi Yuan tai
   pu
 Zi Yuan ta
 ipuShi   http://purl.org/coar/resource_type/c_5794
  Bie Zi 
 Zi Yuan ta  conference paper
  ipu
Zhu Zhe Suo Shu 
         Kayufu
Zhu Zhe Suo Shu 
  (Ying )
         en
         Kayufu
         Remi, Coulom
         
         x Remi, Coulom
 Zhu Zhe Ming 
          Remi, Coulom


         Search repository
         Remi, Coulom
         
         x Remi, Coulom
 Zhu Zhe Ming 
  (Ying )   en Remi, Coulom


         Search repository
Lun Wen Chao Lu 
 Nei Rong Ji 
 Shu tai  Other
   pu
         While training deep-learning neural networks often requires
         considerable amounts of computing power, inference is
         efficient, and can be run on small devices. Cell phones are
         a typical example, but they are still rather powerful. The
 Nei Rong Ji   research presented in this paper takes the challenge to the
   Shu     extreme by running a Go-playing convolutional neural network
         on the 6809 CPU, an 8-bit microprocessor launched by
         Motorola in 1978. The software was implemented on a Thomson
         MO5 microcomputer, and reached a playing strength on par
         with GNU Go.
Lun Wen Chao Lu 
  (Ying )
 Nei Rong Ji 
 Shu tai  Other
   pu
         While training deep-learning neural networks often requires
         considerable amounts of computing power, inference is
         efficient, and can be run on small devices. Cell phones are
         a typical example, but they are still rather powerful. The
 Nei Rong Ji   research presented in this paper takes the challenge to the
   Shu     extreme by running a Go-playing convolutional neural network
         on the 6809 CPU, an 8-bit microprocessor launched by
         Motorola in 1978. The software was implemented on a Thomson
         MO5 microcomputer, and reached a playing strength on par
         with GNU Go.
         gemupuroguraminguwakushiyotsupu2023Lun Wen Ji 
Shu Zhi Qing Bao 
         Juan  2023, p. 66-69, Fa Xing Ri  2023-11-10
 Chu Ban Zhe 
  Yan Yu    ja
 Chu Ban Zhe   Qing Bao Chu Li Xue Hui 

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