| MA YUE | M, 2回目発表 | 情報基盤システム学 | 藤川 和利, 笠原 正治, 柴田 直樹 |
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title: Signal control based on pedestrian traffic at intersections
abstract: In today's highly developed transportation systems, the most important factor in getting vehicles to their destinations quickly is how to reduce vehicle waiting times at intersections. To control vehicle queues at intersections and reduce the average vehicle waiting time, methods using neural networks and genetic algorithms have been proposed, but most existing methods focus only on vehicles. However, not only vehicles, but also pedestrian behavior may influence vehicle movement. This study researches on the impact of pedestrians on vehicles and proposes a new control method based on the number of pedestrians at an intersection to optimize vehicle movements. language of the presentation: English | |||
| PHAM HOAI LUAN | D, 中間発表 | コンピューティング・アーキテクチャ | 中島 康彦, 笠原 正治, 張 任遠 |
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Title: A High-Efficiency FPGA-Based Multimode SHA-2 Accelerator
Abstract: The secure hash algorithm 2 (SHA-2) family, including the SHA-224, SHA-256, SHA-384, and SHA-512 functions, is widely adopted in many modern domains, ranging from Internet of Things devices to cryptocurrency. SHA-2 functions are often implemented on hardware to optimize performance and power. In addition to the high-performance and low-cost requirements, the hardware for SHA-2 must be highly flexible for many applications. This paper proposes an SHA-2 hardware architecture named the multimode SHA-2 accelerator (MSA), which has high performance and flexibility at the system-on-chip level. To achieve high performance and flexibility, our accelerator applies three optimal techniques. First, a multimode processing element architecture is proposed to enable the accelerator to compute various SHA-2 functions for many applications. Second, a three-stage arithmetic logic unit pipeline architecture is proposed to reduce the critical paths and hardware resources. Finally, nonce generator and nonce validator architectures are proposed to reduce memory access and maximize the performance of the proposed MSA for blockchain mining applications. The MSA accuracy is tested on a real hardware platform (the Xilinx Alveo U280 FPGA). The experimental results on the field programmable gate array (FPGA) prove that the proposed MSA achieves significantly better performance, hardware efficiency, and flexibility than previous works. The evaluation results for energy efficiency show that the proposed MSA achieves up to 38.05 Mhps/W, which is 543.6 and 29 times better than the state-of-the-art Intel i9-10940X CPU and RTX 3090 GPU, respectively. Language of the presentation: English | |||
| 井阪 友哉 | M, 1回目発表 | ディペンダブルシステム学 | 井上 美智子, 中島 康彦, 新谷 道広 |
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title: Research on applications of Hyper-Dimensional Computing (HDC)
abstract: Hyper Dimensional Computing (HDC) is a computation method inspired by the human brain, which is characterized by its high speed, robustness, and low power consumption compared to conventional machine learning methods. These features are useful for edge servers and IoT devices used in edge computing. However, HDCs have been shown to operate inefficiently on conventional CPUs and GPUs. To address this issue, we aim to develop an efficient accelerator that can handle HDC. language of the presentation: Japanese 発表題目: Hyper-Dimensional Computing (HDC) の応用に関する研究 発表概要: HDC（Hyper Dimensional Computing）は、人の脳から着想を得た計算手法の一つである。HDCによる計算は、従来の機械学習による手法と比べ高速・ロバスト・低消費電力に動作するという特徴がある。これらの特徴はエッジコンピューティングで用いられるエッジサーバやIoTデバイスが抱えている課題に対して有効なものである。しかし、HDCは従来のCPUやGPUでは効率よく動作できないことが示されている。この課題に対処するために、HDCを扱える効率的なアクセラレータの開発を目指す。 | |||
| 植田 秀樹 | D, 中間発表 | 計算システムズ生物学 | 金谷 重彦, 安本 慶一, 小野 直亮, MD.ALTAF-UL-AMIN, 黄 銘 |
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title: Inference of cleavage mechanisms of γ-secretase using machine learning
abstract: γ-Secretase is a membrane-embedded protease that cleaves amyloid precursor protein (APP) and generates amyloid beta protein (Aβ), which contributes to Alzheimer’s disease. Although γ-secretase is known to cleave APP successively, most likely in every three amino acids, the underlying mechanisms of the cleavage by γ-secretase are not well understood. In this research, we built a variety of machine learning models which predict the amount of a peptide cleaved out by γ-secretase. We selected a model by cleavage site predictions for the reported substrates. Based on the model, we inferred the number of pockets in the active site of γ-secretase, the physicochemical properties involved in cleavages by γ-secretase, and the conserved sequence of γ-secretase. language of the presentation: Japanese | |||
| 前田 雄大 | D, 中間発表 | 計算システムズ生物学 | 金谷 重彦, 安本 慶一, 小野 直亮, MD.ALTAF-UL-AMIN, 黄 銘 |
| 浅田 尚紀 | D, 中間発表 | 計算システムズ生物学 | 金谷 重彦, 作村 諭一, 小野 直亮, MD.ALTAF-UL-AMIN, 黄 銘 |
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title: Evaluation of intercellular lipid lamellae in the stratum corneum by polarized microscopy
abstract: 角層の細胞間脂質は、ラメラ構造を構築し、外部刺激から生体を守るバリア機能を担っている。これまで報告されている細胞間脂質の評価法は、煩雑な操作が必要か、または侵襲性の高いものであった。そのため、本研究では細胞間脂質含有量を簡単かつ迅速に評価する方法の開発を試みた。ラメラ構造は偏光顕微鏡で白く観察されることから、角質層の偏光画像から計算された輝度値から細胞間脂質含有量を推定できると考えた。 偏光画像の輝度と、細胞間脂質の主成分のひとつであるセラミドの量との関係を、Image Jおよび畳み込みニューラルネットワーク（CNN）よる評価モデル構築で解析したところ、相関がみられた。 この方法では、多数の検体の細胞間脂質を効率的に評価する被験者から剥離角層を提供してもらうことで迅速に評価結果を提供することができる。 language of the presentation: Japanese | |||