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 Results 1 - 20 of 76  /  [Next]  
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
RECONF, VLD, CPSY, IPSJ-ARC, IPSJ-SLDM [detail] 2022-01-24
15:55
Online Online Accelerating Deep Neural Networks on Edge Devices by Knowledge Distillation and Layer Pruning
Yuki Ichikawa, Akira Jinguji, Ryosuke Kuramochi, Hiroki Nakahara (Titech) VLD2021-58 CPSY2021-27 RECONF2021-66
A deep neural network (DNN) is computationally expensive, making it challenging to run DNN on edge devices. Therefore, m... [more] VLD2021-58 CPSY2021-27 RECONF2021-66
pp.49-54
RECONF, VLD, CPSY, IPSJ-ARC, IPSJ-SLDM [detail] 2022-01-24
16:20
Online Online Addition of DPU Training Function by Tail Layer Training
Yuki Takashima, Akira Jinguji, Hiroki Nakahara (Tokyo Tech) VLD2021-59 CPSY2021-28 RECONF2021-67
The demand for deep learning has been increasing, and many hardware implementations have been proposed. The Deep learnin... [more] VLD2021-59 CPSY2021-28 RECONF2021-67
pp.55-60
VLD, DC, RECONF, ICD, IPSJ-SLDM
(Joint) [detail]
2021-12-01
09:20
Online Online Block Sparse MLP-based Vision DNN Accelerators on Embedded FPGAs
Akira Jinguji, Hiroki Nakahara (Tokyo Tech) VLD2021-21 ICD2021-31 DC2021-27 RECONF2021-29
Since the advent of Vision Transformer, a deep learning model for image recognition without Convolution, MLP-based model... [more] VLD2021-21 ICD2021-31 DC2021-27 RECONF2021-29
pp.25-30
VLD, DC, RECONF, ICD, IPSJ-SLDM
(Joint) [detail]
2021-12-01
10:10
Online Online A Multilayer Perceptron Training Accelerator using Systolic Array
Takeshi Senoo, Akira Jinguji, Ryosuke Kuramochi, Hiroki Nakahara (Toyko Tech) VLD2021-23 ICD2021-33 DC2021-29 RECONF2021-31
Neural networks are being used in various applications, and the demand for fast training with large amounts of data is e... [more] VLD2021-23 ICD2021-33 DC2021-29 RECONF2021-31
pp.37-42
RECONF 2021-09-10
09:30
Online Online A Low-Latency Inference of Randomly Wired Convolutional Neural Networks on an FPGA
Ryosuke Kuramochi, Hiroki Nakahara (Tokyo Tech) RECONF2021-17
Convolutional neural networks (CNNs) are widely used for image processing tasks in both embedded systems and data center... [more] RECONF2021-17
pp.1-6
RECONF 2021-09-10
09:55
Online Online An FPGA Implementation of neural networks with multi-core structured using high level synthesis
Akira Jinguji, Hiroki Nakahara (Tokyo Tech) RECONF2021-18
(To be available after the conference date) [more] RECONF2021-18
pp.7-12
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
RECONF 2020-09-11
14:55
Online Online An FPGA-Based Low-Latency Accelerator for Randomly Wired Convolutional Neural Networks
Ryosuke Kuramochi, Hiroki Nakahara (Tokyo Tech) RECONF2020-27
Convolutional neural networks(CNNs) are widely used for image tasks in both embedded systems and data centers. Particula... [more] RECONF2020-27
pp.48-53
RECONF 2020-09-11
16:00
Online Online TAI Compiler: Deep Learning Inference Optimizer for an FPGA
Hiroki Nakahara (TAI) RECONF2020-29
 [more] RECONF2020-29
pp.60-65
IPSJ-SLDM, RECONF, VLD, CPSY, IPSJ-ARC [detail] 2020-01-22
17:20
Kanagawa Raiosha, Hiyoshi Campus, Keio University Many Universal Convolution Cores for Ensemble Sparse Convolutional Neural Networks
Ryosuke Kuramochi, Youki Sada, Masayuki Shimoda, Shimpei Sato, Hiroki Nakahara (Titech) VLD2019-65 CPSY2019-63 RECONF2019-55
A convolutional neural network (CNN) is one of the most successful neural networks and widely used for computer vision t... [more] VLD2019-65 CPSY2019-63 RECONF2019-55
pp.67-72
IPSJ-SLDM, RECONF, VLD, CPSY, IPSJ-ARC [detail] 2020-01-22
17:45
Kanagawa Raiosha, Hiyoshi Campus, Keio University An FPGA Implementation of Monocular Depth Estimation
Youki Sada, Masayuki Shimoda, Shimpei Sato, Hiroki Nakahara (titech) VLD2019-66 CPSY2019-64 RECONF2019-56
Among a lot of image recognition applications, Convolutional Neural Network (CNN) has gained high accuracy and increasin... [more] VLD2019-66 CPSY2019-64 RECONF2019-56
pp.73-78
VLD, DC, CPSY, RECONF, ICD, IE, IPSJ-SLDM, IPSJ-EMB, IPSJ-ARC
(Joint) [detail]
2019-11-14
09:40
Ehime Ehime Prefecture Gender Equality Center FPGA implementation of ISA-based sparse CNN using Wide-SIMD
Akira Jinguji, Shimpei Sato, Hiroki Nakahara (Titech) RECONF2019-37
Convolutional Neural Network (CNN) achieves high recognition performance in image recognition, and is expected to be app... [more] RECONF2019-37
pp.9-14
RECONF 2019-09-20
11:40
Fukuoka KITAKYUSHU Convention Center Accurate Pedestrian Detection in Thermal Images for FPGA
Ryosuke Kuramochi, Masayuki Shimoda, Youki Sada, Shimpei Sato, Hiroki Nakahara (titech) RECONF2019-26
Since thermal cameras can detect the heat of objects, they can be used even if there is no light.
Therefore, object de... [more]
RECONF2019-26
pp.31-36
RECONF 2019-05-09
16:10
Tokyo Tokyo Tech Front A CNN-based Classifier for a Digital Spectrometer on a Radio Telescope
Hiroki Nakahara, Shimpei Sato (Titech) RECONF2019-19
 [more] RECONF2019-19
pp.103-108
RECONF 2019-05-10
10:00
Tokyo Tokyo Tech Front An FPGA Implementation of the Semantic Segmentation Model with Multi-path Structure
Youki Sada, Masayuki Shimoda, Shimpei Sato, Hiroki Nakahara (titech) RECONF2019-10
Since the convolutional neural network has a high-performance recognition accuracy,
it is expected to implement variou... [more]
RECONF2019-10
pp.49-54
RECONF 2019-05-10
15:30
Tokyo Tokyo Tech Front Spatial-Separable Convolution: Low memory CNN for FPGA
Akira Jinguji, Masayuki Shimoda, Hiroki Nakahara (titech) RECONF2019-16
Object detection and image recognition using a Convolutional Neural Network (CNN) are used in em- bedded systems, which ... [more] RECONF2019-16
pp.85-90
HWS, VLD 2019-02-27
10:25
Okinawa Okinawa Ken Seinen Kaikan FPGA Implementation of Fully Convolutional Network for Semantic Segmentation
Masayuki Shimoda, Youki Sada, Hiroki Nakahara (titech) VLD2018-93 HWS2018-56
 [more] VLD2018-93 HWS2018-56
pp.1-6
HWS, VLD 2019-02-27
10:50
Okinawa Okinawa Ken Seinen Kaikan Spatial-Separable Convolution: Low memory CNN for FPGA
Akira Jinguji, Masayuki Shimoda, Hiroki Nakahara (titech) VLD2018-94 HWS2018-57
Object detection and image recognition using a Convolutional Neural Network (CNN) are used in embedded systems, which re... [more] VLD2018-94 HWS2018-57
pp.7-12
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