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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| CPSY, DC, RECONF, IPSJ-ARC [detail] | 2024-06-12 09:30 |
Yamanashi | Isawa View Hotel (Primary: On-site, Secondary: Online) |
Exploration and Simulation of FPGA Utilizing 3D-SRAM Ryo Takahashi (Tokyo Tech), Hiroki Nakahara (Tohoku Univ.) |
[more] | |
| 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 |
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