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Committee Date Time Place Paper Title / Authors Abstract Paper #
RECONF 2022-06-08
15:50		 Ibaraki CCS, Univ. of Tsukuba
(Primary: On-site, Secondary: Online)		 Structural Sparsification of Activations and Weights for Low Latency Implementation of CNN
Akira Jinguji, Naoto Soga, Hiroki Nakahara (Tokyo Tech) RECONF2022-22		 (To be available after the conference date) [more] RECONF2022-22
pp.95-100
CPSY, RECONF, VLD, IPSJ-ARC, IPSJ-SLDM [detail] 2021-01-25
15:15		 Online Online A High-speed Convolutional Neural Network Accelerator for an Adaptive Resolution on an FPGA
Koki Sayama, Akira Jinguji, Naoto Soga, Hiroki Nakahara (Tokyo Tech) VLD2020-49 CPSY2020-32 RECONF2020-68		 In recent years, CNN has been used for various tasks in the field of computer vision and has achievedexcellent performan... [more] VLD2020-49 CPSY2020-32 RECONF2020-68
pp.58-62
VLD, DC, RECONF, ICD, IPSJ-SLDM
(Joint) [detail]		 2020-11-17
14:25		 Online Online Energy-Efficient ECG Signals Outlier Detection Hardware Using a Sparse Robust Deep Autoencoder
Naoto Soga, Shimpei Sato, HIroki Nakahara (Tokyo Tech) VLD2020-17 ICD2020-37 DC2020-37 RECONF2020-36		 Advancements in portable electrocardiographs have allowed electrocardiogram (ECG) signals to be recorded in everyday lif... [more] VLD2020-17 ICD2020-37 DC2020-37 RECONF2020-36
pp.36-41
HWS, VLD 2019-02-28
13:55		 Okinawa Okinawa Ken Seinen Kaikan Model Compression for ECG Signals Outlier Detection Hardware trained by Sparse Robust Deep Autoencoder
Naoto Soga, Shimpei Sato, Hiroki Nakahara (Titech) VLD2018-114 HWS2018-77		 In recent years, portable electrocardiographs and wearable devices have begun to spread so that electrocar- diogram (ECG... [more] VLD2018-114 HWS2018-77
pp.127-132
VLD, DC, CPSY, RECONF, CPM, ICD, IE, IPSJ-SLDM, IPSJ-EMB, IPSJ-ARC
(Joint) [detail]		 2018-12-06
11:20		 Hiroshima Satellite Campus Hiroshima Hardware implementation of ECG signals outlier detector trained by Sparse Robust Deep Autoencoder
Naoto Soga, Shimpei Sato, Hiroki Nakahara (Titech) RECONF2018-42		 Current ECG outlier detection is rule-based, there are many false positives, and it is necessary to study a new outlier ... [more] RECONF2018-42
pp.45-50
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