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An efficient hardware design for cerebellar models using approximate circuits: special session paper.

H. Jiang, L. Liu, и J. Han. CODES+ISSS, стр. 31:1-31:2. ACM, (2017)

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Honglan Jin

Ping Jiang

Qiu Jiang

Jiang Yi

Xin Jiang

Другие публикации лиц с тем же именем

Approximate Arithmetic Circuits: Design and Evaluation.H. Jiang, L. Liu, F. Lombardi, и J. Han. Approximate Circuits, Springer, (2019)Hardware-Efficient Logarithmic Floating-Point Multipliers for Error-Tolerant Applications.Z. Niu, T. Zhang, H. Jiang, B. Cockburn, L. Liu, и J. Han. IEEE Trans. Circuits Syst. I Regul. Pap., 71 (1): 209-222 (января 2024)A High-Performance and Energy-Efficient FIR Adaptive Filter Using Approximate Distributed Arithmetic Circuits.H. Jiang, L. Liu, P. Jonker, D. Elliott, F. Lombardi, и J. Han. IEEE Trans. Circuits Syst. I Regul. Pap., 66-I (1): 313-326 (2019)Upward Packet Popup for Deadlock Freedom in Modular Chiplet-Based Systems.Y. Wu, L. Wang, X. Wang, J. Han, J. Zhu, H. Jiang, S. Yin, S. Wei, и L. Liu. HPCA, стр. 986-1000. IEEE, (2022)Majority Logic-Based Approximate Multipliers for Error-Tolerant Applications.T. Zhang, H. Jiang, W. Liu, F. Lombardi, L. Liu, S. Ko, и J. Han. Approximate Computing, Springer International Publishing, (2022)Pj-AxMTJ: Process-in-memory with Joint Magnetization Switching for Approximate Computing in Magnetic Tunnel Junction.H. Cai, H. Jiang, M. Han, Z. Wang, Y. Wang, J. Yang, J. Han, L. Liu, и W. Zhao. ISVLSI, стр. 111-115. IEEE, (2019)Characterizing Approximate Adders and Multipliers for Mitigating Aging and Temperature Degradations.F. Santiago, H. Jiang, H. Amrouch, A. Gerstlauer, L. Liu, и J. Han. IEEE Trans. Circuits Syst. I Regul. Pap., 69 (11): 4558-4571 (2022)Power-Efficient Approximate Multiplier Using Adaptive Error Compensation.Z. Yang, H. Jiang, X. Li, и J. Yang. ACM Great Lakes Symposium on VLSI, стр. 205-210. ACM, (2020)Adaptive Filter Design Using Stochastic Circuits.H. Jiang, C. Shen, P. Jonker, F. Lombardi, и J. Han. ISVLSI, стр. 122-127. IEEE Computer Society, (2016)Learning the Error Features of Approximate Multipliers for Neural Network Applications.H. Mo, Y. Wu, H. Jiang, Z. Ma, F. Lombardi, J. Han, и L. Liu. IEEE Trans. Computers, 73 (3): 842-856 (марта 2024)

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