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2021 年度 実施状況報告書

Time-Space Re-configurable Flash Computations

研究課題

研究課題/領域番号 21K11809
研究機関奈良先端科学技術大学院大学

研究代表者

ZHANG Renyuan  奈良先端科学技術大学院大学, 先端科学技術研究科, 准教授 (00709131)

研究分担者 木村 睦  奈良先端科学技術大学院大学, 先端科学技術研究科, 客員教授 (60368032)
研究期間 (年度) 2021-04-01 – 2024-03-31
キーワードContinuous domain / bisection neural network / re-configurability / parameter reduction
研究実績の概要

In this year, both of time- and space-domains of re-censurability were investigated for computing platforms as follows: (1) stochastic computing (SC) in continuous domain. In this phase, the SC in discrete domain is migrated into continuous domain with rich benefit. In this work, the concept of continuous state machine is improved along with the circuit implementation. From the circuit simulation results, the accuracy, power, and speed of the proposed hybrid SC circuits are all similar or superior to the state-of-art. (2) Evolutions of DiaNets, addressing space-elastic. We proposed various generations of DiaNet to prevent the depth explosion and gradient vanishing problem, including I/O layer integration and skip connection (DiaNet2.0 and 3.0). Various applications are implemented.

現在までの達成度 (区分)
現在までの達成度 (区分)

1: 当初の計画以上に進展している

理由

The current progress fully matches my initial proposal. All the performances of our developed platforms are superior in some specific features as (1) the stochastic computing appears the expected performances; (2) DiaNets are updated for several versions and DiaNet4.0 on-going. The research is conducted better than the expectation as: (1) from this project, we start collaborating with physicists in super conductor field and kick-off new scenario of “cool data center”by combing CMOS and AQFP on the basis of this project. The very first results will be disclosed on IEEE SOCC soon, and submitted to a patent and a journal paper. (2) A “calculator-free neural network” is under investigation and its feasibility has been verified by the works in this year.

今後の研究の推進方策

Firstly, the quantum-spike coding methodology will be explored. The DiaNet4.0 in planning will be innovated by quantizing the data from integer to ternary. Then, the look-up table (LUT) is used for calculating instead of calculators or ALUs. Secondly, cool stochastic computing platform will be investigated as (1) the behavior of CMOS device under low temperature is investigated. (2) the simulators for adiabatic quantum-flux-parametron (AQFP) are expected to develop and improve. So far, the behavior of single AQFP device is well investigated but the stochastic implementation of AQFP has not been scientifically modeled. (3) The interface between CMOS and AQFP will be designed, which includes binary-voltage-to-analog-current converter, bias calibrator, non-linear look-up table etc.

次年度使用額が生じた理由

Due to COVID-19, the supply of semi-conductor production is lack. Most of experiment equipment such as FPGA-SoC cannot be purchased during 2021. Moreover, all the business trips to international conferences were eliminated since they were held on-line. Then, the budgets are transferred to 2022 for equipment and publication charges including international conferences and journals.

  • 研究成果

    (7件)

すべて 2022 2021

すべて 雑誌論文 (4件) (うち査読あり 4件) 学会発表 (3件) (うち国際学会 2件)

  • [雑誌論文] MuGRA: A Scalable Multi-Grained Reconfigurable Accelerator Powered by Elastic Neural Network2022

    • 著者名/発表者名
      Kan Yirong、Wu Man、Zhang Renyuan、Nakashima Yasuhiko
    • 雑誌名

      IEEE Transactions on Circuits and Systems I: Regular Papers

      巻: 69 ページ: 258~271

    • DOI

      10.1109/TCSI.2021.3099034

    • 査読あり
  • [雑誌論文] A Feasibility Study of Multi-Domain Stochastic Computing Circuit2021

    • 著者名/発表者名
      ERLINA Tati、ZHANG Renyuan、NAKASHIMA Yasuhiko
    • 雑誌名

      IEICE Transactions on Electronics

      巻: E104.C ページ: 153~163

    • DOI

      10.1587/transele.2020ECP5015

    • 査読あり
  • [雑誌論文] DiaNet: An elastic neural network for effectively re-configurable implementation2021

    • 著者名/発表者名
      Wu Man、Kan Yirong、Erlina Tati、Zhang Renyuan、Nakashima Yasuhiko
    • 雑誌名

      Neurocomputing

      巻: 464 ページ: 242~251

    • DOI

      10.1016/j.neucom.2021.08.059

    • 査読あり
  • [雑誌論文] STT-BSNN: An In-Memory Deep Binary Spiking Neural Network Based on STT-MRAM2021

    • 著者名/発表者名
      Nguyen Van-Tinh、Trinh Quang-Kien、Zhang Renyuan、Nakashima Yasuhiko
    • 雑誌名

      IEEE Access

      巻: 9 ページ: 151373~151385

    • DOI

      10.1109/ACCESS.2021.3125685

    • 査読あり
  • [学会発表] Training Low-Latency Spiking Neural Network through Knowledge Distillation2021

    • 著者名/発表者名
      Sugahara Takuya, Renyuan Zhang, and Yasuhiko Nakashima
    • 学会等名
      IEEE Symposium on Low-Power and High-Speed Chips
    • 国際学会
  • [学会発表] An Accurate and Compact Hyperbolic Tangent and Sigmoid Computation Based Stochastic Logic2021

    • 著者名/発表者名
      Van Tinh NGUYEN, T. -K. Luong, E. Popovici, Q. -K. Trinh, Renyuan Zhang and Yasuhiko Nakashima
    • 学会等名
      IEEE International Midwest Symposium on Circuits & Systems
    • 国際学会
  • [学会発表] Ternarizing Deep Spiking Neural Network2021

    • 著者名/発表者名
      Man Wu, Yirong Kan, Van_Tinh Nguyen, Renyuan Zhang, Yasuhiko Nakashima
    • 学会等名
      信学技報

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公開日: 2022-12-28  

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