この検索内の頻出タグ: neural^:13 network^:6 networks^:6 training^:4 accelerator^:3 multiplier^:3 bayesian^:2 convolutional^:2 deep^:2 efficient^:2 floating-point^:2 fpga^:2 logarithm-approximate^{:2 :1} activations^:1 adapting^:1 analog^:1 analysis^:1 applicable^:1 b2n2^:1 bernoulli^:1 booth^:1 bynqnet^:1 cascsde^:1 comparison^:1 crossbar-based^:1 data^:1 detection^:1 digital^:1 dynamics^:1

13 件の該当がありました． : このページのURL : HTML

論文誌

[1] Q. Cheng, M. Huang, C. Man, A. Shen, L. Dai, H. Yu, and M. Hashimoto, "Reliability Exploration of System-On-Chip with Multi-Bit-Width Accelerator for Multi-Precision Deep Neural Networks," IEEE Transactions on Circuits and Systems I: Regular Papers, volume 70, number 10, 3978 -- 3991, October 2023. [pdf]

[2] Q. Cheng, L. Dai, M. Huang, A. Shen, W. Mao, M. Hashimoto, and H. Yu, "A Low-Power Sparse Convolutional Neural Network Accelerator with Pre-Encoding Radix-4 Booth Multiplier," IEEE Transactions on Circuits and Systems II, volume 70, number 6, 2246 - 2250, June 2023. [pdf]

[3] H. Awano and M. Hashimoto, "B2N2: Resource Efficient Bayesian Neural Network Accelerator Using Bernoulli Sampler on FPGA," Integration, the VLSI Journal, volume 89, pages 1-8, March 2023. [pdf]

[4] T. Cheng, Y. Masuda, J. Chen, J. Yu, and M. Hashimoto, "Logarithm-Approximate Floating-Point Multiplier Is Applicable to Power-Efficient Neural Network Training," Integration, the VLSI Journal, volume 74, pages 19--31, September 2020. [pdf]

国際会議

[1] C. Kawano and M. Hashimoto, "Performance Comparison of Memristor Crossbar-Based Analog and Fpga-Based Digital Weight-Memory-Less Neural Networks," Proceedings of IEEE International Conference on Rebooting Computing (ICRC), December 2023. [pdf]

[2] Y. Zhang, K. Ito, H. Itsuji, T. Uezono, T. Toba, and M. Hashimoto, "Fault Mode Analysis of Neural Network-Based Object Detection on GPUs with Neutron Irradiation Test," Proceedings of European Conference on Radiation and Its Effects on Components and Systems (RADECS), October 2020.

[3] K. Onishi, J. Yu, and M. Hashimoto, "Memory Efficient Training Using Lookup-Table-Based Quantization for Neural Network," Proceedings of International Conference on Artificial Intelligence Circuits and Systems (AICAS), August 2020. [pdf]

[4] H. Awano and M. Hashimoto, "BYNQNet: Bayesian Neural Network with Quadratic Activations for Sampling-Free Uncertainty Estimation on FPGA," Proceedings of Design, Automation and Test in Europe Conference (DATE), April 2020. [pdf]

[5] Z. Yan, Y. Shi, W. Liao, M. Hashimoto, X. Zhou, and C. Zhuo, "When Single Event Upset Meets Deep Neural Networks: Observations, Explorations, and Remedies," Proceedings of Asia and South Pacific Design Automation Conference (ASP-DAC), January 2020. [pdf]

[6] T. Tanio, J. Yu, and M. Hashimoto, "Training Data Reduction Using Support Vectors for Neural Networks," Proceedings of Asia-Pacific Signal and Information Processing Association (APSIPA) Annual Summit and Conference (ASC), November 2019. [pdf]

[7] T.-Y. Cheng, J. Yu, and M. Hashimoto, "Minimizing Power for Neural Network Training with Logarithm-Approximate Floating-Point Multiplier," Proceedings of International Symposium on Power and Timing Modeling, Optimization and Simulation (PATMOS), July 2019. [pdf]

[8] K. Itoh, J. Yu, and M. Hashimoto, "Adapting Soft Cascade to Mac Operations of Convolutional Neural Networks," Proceedings of International Symposium on Multimedia and Communication Technology (ISMAC), August 2018.

著書

[1] Hideyuki Suzuki, Jun Tanida, and Masanori Hashimoto, Photonic Neural Networks with Spatiotemporal Dynamics, Springer Singapore,, 2023.