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Technical Committee on Dependable Computing (DC) (Searched in: 2021)
Search Results: Keywords 'from:2021-12-01 to:2021-12-01'
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| VLD, DC, RECONF, ICD, IPSJ-SLDM (Joint) [detail] |
2021-12-01 09:20 |
Online | Online | Soft Errors on Flip-flops Depending on Circuit and Layout Structures Estimated by TCAD Simulations Moeka Kotani, Ryuichi Nakajima (KIT), Kazuya Ioki (ROHM), Jun Furuta, Kazutoshi Kobayashi (KIT)  VLD2021-17 ICD2021-27 DC2021-23 RECONF2021-25 |
We compare the soft error tolerance of conventional flip-flops (FFs) and the proposed radiation-hard FF with small area,... [more] | VLD2021-17 ICD2021-27 DC2021-23 RECONF2021-25 pp.1-6 |
| VLD, DC, RECONF, ICD, IPSJ-SLDM (Joint) [detail] |
2021-12-01 09:45 |
Online | Online | Low quiescent current LDO with FVF based PSRR enhanced circuit for wearable EEG measurement devices Kenji Mii, Daisuke Kanemoto, Osamu Maida, Tetsuya Hirose (Osaka Univ.) VLD2021-18 ICD2021-28 DC2021-24 RECONF2021-26 |
This paper proposes a low quiescent current low-dropout regulator (LDO) with a flipped voltage follower (FVF)-based powe... [more] | VLD2021-18 ICD2021-28 DC2021-24 RECONF2021-26 pp.7-12 |
| VLD, DC, RECONF, ICD, IPSJ-SLDM (Joint) [detail] |
2021-12-01 10:10 |
Online | Online | MTJ-based non-volatile SRAM circuit with data-aware store control for energy saving Hisato Miyauchi, Kimiyoshi Usami (SIT) VLD2021-19 ICD2021-29 DC2021-25 RECONF2021-27 |
In recent years, the increase of leakage power in LSIs has become a problem, and one of the methods to reduce the leakag... [more] | VLD2021-19 ICD2021-29 DC2021-25 RECONF2021-27 pp.13-18 |
| VLD, DC, RECONF, ICD, IPSJ-SLDM (Joint) [detail] |
2021-12-01 10:35 |
Online | Online | Energy saving in a multi-context coarse grained reconfigurable array with non-volatile flip-flops Aika Kamei, Takuya Kojima, Hideharu Amano (Keio Univ.), Daiki Yokoyama, Hisato Miyauchi, Kimiyoshi Usami (SIT), Keizo Hiraga, Kenta Suzuki (SSS) VLD2021-20 ICD2021-30 DC2021-26 RECONF2021-28 |
IoT and edge-computing have been attracting much attention and demands for power efficiency as well as high performance ... [more] | VLD2021-20 ICD2021-30 DC2021-26 RECONF2021-28 pp.19-24 |
| 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 09:45 |
Online | Online | Sparsity-Gradient-Based Pruning and the Vitis-AI Implementation for Compacting Deep Learning Models Hengyi Li, Xuebin Yue, Lin Meng (Ritsumeikan Univ.) VLD2021-22 ICD2021-32 DC2021-28 RECONF2021-30 |
The paper proposes a Sparsity-Gradient-Based layer-wise Pruning technique for compacting deep neural networks and accele... [more] | VLD2021-22 ICD2021-32 DC2021-28 RECONF2021-30 pp.31-36 |
| 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 |
| VLD, DC, RECONF, ICD, IPSJ-SLDM (Joint) [detail] |
2021-12-01 10:35 |
Online | Online | Basic evaluation of ReNA, a DNN accelerator using numerical representation posit Yasuhiro Nakahara, Yuta Masuda, Masato Kiyama, Motoki Amagasaki, Masahiro Iida (Kumamoto Univ.) VLD2021-24 ICD2021-34 DC2021-30 RECONF2021-32 |
In Convolutional Neural Network (CNN) accelerators for edge, numerical precision of data should be reduced as much as po... [more] | VLD2021-24 ICD2021-34 DC2021-30 RECONF2021-32 pp.43-48 |
| VLD, DC, RECONF, ICD, IPSJ-SLDM (Joint) [detail] |
2021-12-01 11:10 |
Online | Online | Examination of model validation of interlocking connection using UPPAAL Takumi Hasegawa, Kohei Yabuki, Takahiro Shimura (Kyosan Mfg), Takeshi Mizuma (UTokyo) |
[more] | |
| VLD, DC, RECONF, ICD, IPSJ-SLDM (Joint) [detail] |
2021-12-01 11:35 |
Online | Online | Low power neural network by reducing the operating voltage of SRAM Keisuke Kozu, Kazuteru Namba (Chiba Univ.) VLD2021-25 ICD2021-35 DC2021-31 RECONF2021-33 |
With the advancement of machine learning technology, networks are becoming more and more complex and computationally int... [more] | VLD2021-25 ICD2021-35 DC2021-31 RECONF2021-33 pp.49-53 |
| VLD, DC, RECONF, ICD, IPSJ-SLDM (Joint) [detail] |
2021-12-01 14:20 |
Online | Online | Triple-Rail Stochastic Number and Its Applications Shoki Kawaminami, Shigeru Yamashita (Ritsumeikan Univ) VLD2021-26 ICD2021-36 DC2021-32 RECONF2021-34 |
In Stochastic Computing (SC), we use bit-strings called stochastic numbers (SNs) to perform calculations in very low-cos... [more] | VLD2021-26 ICD2021-36 DC2021-32 RECONF2021-34 pp.54-59 |
| VLD, DC, RECONF, ICD, IPSJ-SLDM (Joint) [detail] |
2021-12-01 14:45 |
Online | Online | Improving Accuracy of Addition for Stochastic Computing Ichilawa Katsuhiro, Shigeru Yamashita (Ritsumeikan Univ.) VLD2021-27 ICD2021-37 DC2021-33 RECONF2021-35 |
Stochastic Computing (SC) is an approximate computing paradigm to perform calculations by using Stochastic Numbers (SNs)... [more] | VLD2021-27 ICD2021-37 DC2021-33 RECONF2021-35 pp.60-65 |
| VLD, DC, RECONF, ICD, IPSJ-SLDM (Joint) [detail] |
2021-12-01 15:10 |
Online | Online | Error Recovery Method by Canceling Errors on DMFBs Yuji Wada, Shigeru Yamashita (Ritsumeikan Univ.) VLD2021-28 ICD2021-38 DC2021-34 RECONF2021-36 |
(To be available after the conference date) [more] | VLD2021-28 ICD2021-38 DC2021-34 RECONF2021-36 pp.66-71 |
| VLD, DC, RECONF, ICD, IPSJ-SLDM (Joint) [detail] |
2021-12-01 15:35 |
Online | Online | Determining Optimal Number of Layers for Network-Flow-based Sample Preparation Akira Ishida, Shigeru Yamashita (Ritsumeikan Univ.) VLD2021-29 ICD2021-39 DC2021-35 RECONF2021-37 |
Sample preparation is an indispensable process when we perform biochemical experiments on DMFBs. There exists an optimal... [more] | VLD2021-29 ICD2021-39 DC2021-35 RECONF2021-37 pp.72-77 |
| VLD, DC, RECONF, ICD, IPSJ-SLDM (Joint) [detail] |
2021-12-01 14:20 |
Online | Online | A Dual-mode SAR ADC to Detect Power Analysis Attack Takuya Wadatsumi, Takuji Miki, Makoto Nagata (Kobe Univ.) VLD2021-30 ICD2021-40 DC2021-36 RECONF2021-38 |
Distributed IoT devices are exposed to unexpected interferences by physical accesses by malicious attackers. An on-chip ... [more] | VLD2021-30 ICD2021-40 DC2021-36 RECONF2021-38 pp.78-82 |
| VLD, DC, RECONF, ICD, IPSJ-SLDM (Joint) [detail] |
2021-12-01 14:45 |
Online | Online | Diagnosis of Switching-Induced IR Drop by On-Chip Voltage Monitors Kazuki (Kobe Univ.), Leonidas Kataselas (Aristotle Univ.), Ferenc Fodor (IMEC), Alkis Hatzopoulos (Aristotle Univ.), Makoto Nagata (Kobe Univ.), Erik Jan Marinissen (IMEC) VLD2021-31 ICD2021-41 DC2021-37 RECONF2021-39 |
On-chip monitor (OCM) circuits enable us to observe dynamic power-supply (PS) waveforms within power domains individuall... [more] | VLD2021-31 ICD2021-41 DC2021-37 RECONF2021-39 pp.83-86 |
| VLD, DC, RECONF, ICD, IPSJ-SLDM (Joint) [detail] |
2021-12-02 09:20 |
Online | Online | Development of Spiking Neural Network with Mem Capacitor
-- Reduction of recognition accuracy loss by improving the conversion method between synaptic strength and capacitance -- Atsushi Sawada, Reon Oshio, Mutsumi Kimura, Renyuan Zhang, Yasuhiko Nakashima (NAIST) VLD2021-32 ICD2021-42 DC2021-38 RECONF2021-40 |
Research on artificial intelligence is developing rapidly, and there is an increasing need for the development of comput... [more] | VLD2021-32 ICD2021-42 DC2021-38 RECONF2021-40 pp.87-92 |
| VLD, DC, RECONF, ICD, IPSJ-SLDM (Joint) [detail] |
2021-12-02 09:45 |
Online | Online | Routing of Delivery Drones with Load- and Wind-Dependent Flight Speed Satoshi Ito, Keishi Akaiwa, Yusuke Funabashi, Hiroki Nishikawa, Xiangbo Kong (Ritsumeikan Univ.), Ittetsu Taniguchi (Osaka Univ.), Hiroyuki Tomiyama (Ritsumeikan Univ.) VLD2021-33 ICD2021-43 DC2021-39 RECONF2021-41 |
Drone-based package delivery is expected to be a promising way to solve the last mile problem. The flight speed of drone... [more] | VLD2021-33 ICD2021-43 DC2021-39 RECONF2021-41 pp.93-98 |
| VLD, DC, RECONF, ICD, IPSJ-SLDM (Joint) [detail] |
2021-12-02 10:10 |
Online | Online | Design Method of ECG Measurement System Using Compression Sensing Yuki Matsumura, Daisuke Kanemoto, Osamu Maida, Tetsuya Hirose (Osaka Univ) VLD2021-34 ICD2021-44 DC2021-40 RECONF2021-42 |
In recent years, there has been an increasing demand for real-time health information, e.g., electrocardiograms, to be p... [more] | VLD2021-34 ICD2021-44 DC2021-40 RECONF2021-42 pp.99-104 |
| VLD, DC, RECONF, ICD, IPSJ-SLDM (Joint) [detail] |
2021-12-02 10:35 |
Online | Online | Calculation method of correctly rounded exponential function on an FPGA Takuya Haraguchi, Naofumi Takagi (Kyoto Univ.) VLD2021-35 ICD2021-45 DC2021-41 RECONF2021-43 |
We propose the FPGA-oriented calculation method of correctly rounded exponential function, exp, which is one of the func... [more] | VLD2021-35 ICD2021-45 DC2021-41 RECONF2021-43 pp.105-110 |
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