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 |
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 16:55 |
Kanagawa |
Raiosha, Hiyoshi Campus, Keio University |
A Comparison of Filter for Convolutional Neural Network towards Hardware Implementation Kosuke Akimoto, Youki Sada, Shimpei Sato, Hiroki Hakahara (Tokyo Tech) VLD2019-64 CPSY2019-62 RECONF2019-54 |
Convolutional neural networks have high recognition accuracy in computer vision task, and many of the learned filters ar... [more] |
VLD2019-64 CPSY2019-62 RECONF2019-54 pp.61-66 |
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 |
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 |
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 |
IPSJ-SLDM, RECONF, VLD, CPSY, IPSJ-ARC [detail] |
2019-01-30 13:30 |
Kanagawa |
Raiosha, Hiyoshi Campus, Keio University |
A CNN with a Noise Addition for Efficient Implementation on an FPGA Atsuki Munakata, Shimpei Satou, Hiroki Nakahara (Tokyo Tech) VLD2018-75 CPSY2018-85 RECONF2018-49 |
This article is a technical report without peer review, and its polished and/or extended version may be published elsewh... [more] |
VLD2018-75 CPSY2018-85 RECONF2018-49 pp.19-24 |
IPSJ-SLDM, RECONF, VLD, CPSY, IPSJ-ARC [detail] |
2019-01-30 13:55 |
Kanagawa |
Raiosha, Hiyoshi Campus, Keio University |
Filter-wise Pruning Approach to FPGA Implementation of Fully Convolutional Network for Semantic Segmentation Masayuki Shimoda, Youki Sada, Hiroki Nakahara (titech) VLD2018-76 CPSY2018-86 RECONF2018-50 |
[more] |
VLD2018-76 CPSY2018-86 RECONF2018-50 pp.25-30 |