
- 論文誌
- [1] 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]
- [2] 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]
- [3] 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] Q. Cheng, Q. Li, Z. Yang, Z. Kong, G. Niu, Y. Liang, J. Li, J. H. Park, W. Liao, H. Awano, T. Sato, L. Lin, and M. Hashimoto, "A Radiation-Hardened Neuromorphic Imager with Self-Healing Spiking Pixels and Unified Spiking Neural Network for Space Robotics," Digest of Symposium on VLSI Technology and Circuits, 採録済.
- [2] 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]
- [3] 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]
- [4] 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]
- [5] A. Muramatsu, M. Hashimoto, and H. Onodera, "LSI Power Network Analysis with On-Chip Wire Inductance," In Proceedings of Workshop on Synthesis and System Integration of Mixed Technologies (SASIMI), pages 55-60, October 2004.