TIM-8122 V1

Wanwu, L., Lin, L., Jixian, Z., Shuai, L., & Jiahao, Q. (2022). Multi-core parallel architecture design and experiment for deep learning model training. Multimedia Tools & Applications, 81(8), 11587–11604. https://doi.org/10.1007/s11042-022-12292-6
The author introduces and discusses the advantages of parallel architecture designs on Deep learning modules.

Qu, X., Wang, J., & Xiao, J. (2020). Parallelnas: A parallel and distributed system for neural architecture search. High Performance Computing and Communications [Conference paper]. IEEE 18th International Conference on Smart City; IEEE 6th International Conference on Data Science and Systems (HPCC/SmartCity/DSS), 2020 IEEE 22nd International Conference on, HPCC-SMARTCITY-DSS, 247–254. https://doi.org/10.1109/HPCC-SmartCity-DSS50907.2020.00031
The author discusses the advantages of Neural Architecture Search for automating the design of deep learning models.

Zhang, X. (2023). Improved three-dimensional inception networks for hyperspectral remote sensing image classification. IEEE Access, Access, IEEE, 11, 32648–32658.
The author discusses a specialized architecture for remote sensing image classification.

Ahmad, R., Mohamed Omar, M. F., Rajendran, J., & Ismail, W. (2022). Performance analysis of enhanced AES-128 and Blowfish algorithms through parallel-pipelined-memory techniques. Wireless Personal Communications, 127(4), 3615–3635. https://doi.org/10.1007/s11277-022-09933-2
The paper presents an architecture to improve the performance analysis of enhanced AES-128 and Blowfish algorithms.

https://go.openathens.net/redirector/nu.edu?url=https%3A%2F%2Flink.springer.com%2Farticle%2F10.1007%2Fs11227-021-04098-4
This paper offers a good analysis of multiple networks-on-chip.