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[65]
L. R. Upton, A. Levy, M. D. Scott, et al.

EMBER: A 100 MHz, 0.86 mm2, Multiple-Bits-per-Cell RRAM Macro in 40 nm CMOS with Compact Peripherals and 1.0 pJ/bit Read Circuitry,” in ESSCIRC 2023- IEEE 49th European Solid State Circuits Conference (ESSCIRC), 2023, pp. 469–472.

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F. Tian, X. Wang, J. Chen, et al.

BIOS: A 40nm Bionic Sensor-defined 0.47pJ/SOP, 268.7TSOPs/W Configurable Spiking Neuron-in-Memory Processor for Wearable Healthcare,” in ESSCIRC 2023- IEEE 49th European Solid State Circuits Conference (ESSCIRC), 2023, pp. 225–228.

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X. Hou, J. Liu, X. Tang, et al.

MMExit: Enabling Fast and Efficient Multi-modal DNN Inference with Adaptive Network Exits,” in Euro-Par 2023: Parallel Processing, Cham: Springer Nature Switzerland, 2023, pp. 426–440.

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C. Tao, L. Hou, H. Bai, et al.

 “Structured Pruning for Efficient Generative Pre-trained Language Models,” in Findings of the Association for Computational Linguistics: ACL 2023, Toronto, Canada: Association for Computational Linguistics, Jul. 2023, pp. 10 880–10 895.

[61]
B. Bartan and M. Pilanci

Randomized Polar Codes for Anytime Distributed Machine Learning,” IEEE Journal on Selected Areas in Information Theory, vol. 4, pp. 393–404, 2023.

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L. Liu, A. F. Laguna, R. Rajaei, et al.

A Reconfigurable FeFET Content Addressable Memory for Multi-State Hamming Distance,” IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 70, no. 6, pp. 2356–2369, 2023.

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X. Hu, X. Liu, Y. Liu, et al.

A Tiny Accelerator for Mixed-Bit Sparse CNN Based on Efficient Fetch Method of SIMO SPad,” IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 70, no. 8, pp. 3079–3083, 2023.

[58]
P. Zhang, Z. Ge, L. Song, and E. Y. Lam

Neuromorphic Imaging With Density-Based Spatiotemporal Denoising,” IEEE Transactions on Computational Imaging, vol. 9, pp. 530–541, 2023.

[57]
X. Chen, J. Zhu, J. Jiang, and C.-Y. Tsui

Tight Compression: Compressing CNN Through Fine-Grained Pruning and Weight Permutation for Efficient Implementation,” IEEE Transactions on Computer- Aided Design of Integrated Circuits and Systems, vol. 42, no. 2, pp. 644–657, 2023.

[56]
H. Geng, T. Chen, Y. Ma, B. Zhu, and B. Yu

 “PTPT: Physical Design Tool Parameter Tuning via Multi-Objective Bayesian Optimization,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 42, no. 1, pp. 178–189, 2023.

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R. Zhang, C. Tang, X. Sun, et al.

Sky-TCAM: Low-Power Skyrmion-Based Ternary Content Addressable Memory,” IEEE Transactions on Electron Devices, vol. 70, no. 7, pp. 3517–3522, 2023.

[54]
S. Zhang, N. Meng, and E. Y. Lam

LRT: An Efficient Low-Light Restoration Transformer for Dark Light Field Images,” IEEE Transactions on Image Processing, vol. 32, pp. 4314–4326, 2023.

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S. Wang, Y. Li, D. Wang, et al.

Echo state graph neural networks with analogue random resistive memory arrays,” Nature Machine Intelligence, vol. 5, no. 2, pp. 104–113, Feb. 2023.

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Z. Ge, H. Wei, F. Xu, et al.

Millisecond autofocusing microscopy using neuromorphic event sensing,” Optics and Lasers in Engineering, vol. 160, p. 107 247, 2023.

[51]
P. Yi and S. Achour

Hardware-Aware Static Optimization of Hyperdimensional Computations,” Proc. ACM Program. Lang., vol. 7, no. OOPSLA2, Oct. 2023.

[50]
Z. Xie

Efficient Runtime Power Modeling with On-Chip Power Meters,” in Proceedings of the 2023 International Symposium on Physical Design, ser. ISPD ’23, Virtual Event, USA: Association for Computing Machinery, 2023, pp. 168–174.

[49]
C.-C. Chang, J. Pan, Z. Xie, J. Hu, and Y. Chen

Rethink before Releasing Your Model: ML Model Extraction Attack in EDA,” in Proceedings of the 28th Asia and South Pacific Design Automation Conference, ser. ASPDAC ’23, Tokyo, Japan: Association for Computing Machinery, 2023, pp. 252–257.

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C.-C. Chang, J. Pan, Z. Xie, et al.

Fully Automated Machine Learning Model Development for Analog Placement Quality Prediction,” in Proceedings of the 28th Asia and South Pacific Design Automation Conference, ser. ASPDAC’23, Tokyo, Japan: Association for Computing Machinery, 2023, pp. 58–63.

[47]
F. Zhang and M. Pilanci

Optimal Shrinkage for Distributed Second-Order Optimization,” in Proceedings of the 40th International Conference on Machine Learning, ser. Proceedings of Machine Learning Research, vol. 202, PMLR, Jul. 2023, pp. 41 523–41 549.

[46]
A. Mishkin and M. Pilanci

Optimal Sets and Solution Paths of ReLU Networks,” in Proceedings of the 40th International Conference on Machine Learning, ser. Proceedings of Machine Learning Research, vol. 202, PMLR, Jul. 2023, pp. 24 888–24 924.