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[25]
S. Wang, H. Chen, W. Zhang, et al.

Convolutional Echo-State Network with Random Memristors for Spatiotemporal Signal Classification,” Advanced Intelligent Systems, vol. 4, no. 8, p. 2 200 027, 2022.

[24]
X. Hou, C. Xu, J. Liu, et al.

Characterizing and Understanding End-to-End Multi-Modal Neural Networks on GPUs,” IEEE Computer Architecture Letters, vol. 21, no. 2, pp. 125–128, 2022.

[23]
M. Schaller, G. Banjac, S. Diamond, A. Agrawal, B. Stellato, and S. Boyd

Embedded Code Generation With CVXPY,” IEEE Control Systems Letters, vol. 6, pp. 2653–2658, 2022.

[22]
R. Mao, B. Wen, M. Jiang, J. Chen, and C. Li

Experimentally-Validated Crossbar Model for Defect- Aware Training of Neural Networks,” IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 69, no. 5, pp. 2468–2472, 2022.

[21]
H. Geng, Y. Ma, Q. Xu, J. Miao, S. Roy, and B. Yu

High-Speed Adder Design Space Exploration via Graph Neural Processes,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 41, no. 8, pp. 2657–2670, 2022.

[20]
J. Zheng, Y. Liu, X. Liu, L. Liang, D. Chen, and K.-T. Cheng

ReAAP: A Reconfigurable and Algorithm-Oriented Array Processor With Compiler-Architecture Co-Design,” IEEE Transactions on Computers, vol. 71, no. 12, pp. 3088–3100, 2022.

[19]
S.-Q. Dai, C. J. Estrada, A.-N. Xiong, C. Xu, J. G. Yuan, and M. Chan

A CMOS-Compatible Photonic Demodulator With Low-Power Consumption for Time-of-Flight Image Sensor,” IEEE Transactions on Electron Devices, vol. 69, no. 11, pp. 6178–6183, 2022.

[18]
C. Tao, R. Lin, Q. Chen, Z. Zhang, P. Luo, and N. Wong

FAT: Frequency-Aware Transformation for Bridging Full-Precision and Low-Precision Deep Representations,” IEEE Transactions on Neural Networks and Learning Systems, pp. 1–15, 2022.

[17]
M. Wang, S. Rasoulinezhad, P. H. W. Leong, and H. K.-H. So

NITI: Training Integer Neural Networks Using Integer-Only Arithmetic,” IEEE Transactions on Parallel and Distributed Systems, vol. 33, no. 11, pp. 3249–3261, 2022.

[16]
X. Chen, Y. Fu, J. Feng, J. Zhang, S. Chen, and J. Xu
[15]
Y. Zhang, Y. Deng, Y. Lin, et al.

Oscillator-Network-Based Ising Machine,” Micromachines, vol. 13, no. 7, 2022.

[14]
R. Mao, B. Wen, A. Kazemi, et al.
[13]
Z. Jiang, W. Wang, B. Li, and B. Li

Pisces: Efficient Federated Learning via Guided Asynchronous Training,” in Proceedings of the 13th Symposium on Cloud Computing, ser. SoCC ’22, San Francisco, California: Association for Computing Machinery, 2022, pp. 370–385.

[12]
Y. Gao, S. Wang, and H. K.-H. So

REMOT: A Hardware-Software Architecture for Attention-Guided Multi-Object Tracking with Dynamic Vision Sensors on FPGAs,” in Proceedings of the 2022 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, ser. FPGA ’22, Virtual Event, USA: As- sociation for Computing Machinery, 2022, pp. 158–168.

[11]
X. Huang, Z. Shen, S. Li, et al.

SDQ: Stochastic Differentiable Quantization with Mixed Precision,” in Proceedings of the 39th International Conference on Machine Learning, ser. Proceedings of Machine Learning Research, vol. 162, PMLR, Jul. 2022, pp. 9295–9309.

[10]
B. Bartan and M. Pilanci

Neural Fisher Discriminant Analysis: Optimal Neural Network Embeddings in Polynomial Time,” in Proceedings of the 39th International Conference on Machine Learning, ser. Proceedings of Machine Learning Research, vol. 162, PMLR, Jul. 2022, pp. 1647–1663.

[9]
L. Liu, B. Fu, M. D. F. Wong, and E. F. Y. Young

Xplace: An Extremely Fast and Extensible Global Placement Framework,” in Proceedings of the 59th ACM/IEEE Design Automation Conference, ser. DAC ’22, San Francisco, California: Association for Computing Machinery, 2022, pp. 1309–1314.

[8]
H. Geng, Q. Xu, T.-Y. Ho, and B. Yu

PPATuner: Pareto-Driven Tool Parameter Auto-Tuning in Physical Design via Gaussian Process Transfer Learning,” in Proceedings of the 59th ACM/IEEE Design Automation Conference, ser. DAC ’22, San Francisco, California: Association for Computing Machinery, 2022, pp. 1237–1242.

[7]
S. Lin, J. Liu, T. Liu, M. D. F. Wong, and E. F. Y. Young

NovelRewrite: Node-Level Parallel AIG Rewriting,” in Proceedings of the 59th ACM/IEEE Design Automation Conference, ser. DAC ’22, San Francisco, California: Association for Computing Machinery, 2022, pp. 427–432.

[6]
C. Tao, L. Hou, W. Zhang, et al.

Compression of Generative Pre-trained Language Models via Quantization,” in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Dublin, Ireland: Association for Computational Linguistics, May 2022, pp. 4821–4836.