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Efficient Inference Of Image-Based Neural Network Models In Reconfigurable Systems With Pruning And Quantization.

, , , , , , and . ICIP, page 2491-2495. IEEE, (2022)

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Cyber-coordinated Simulation Models for Multi-stage Additive Manufacturing of Energy Products., , , , , , and . CASE, page 893-898. IEEE, (2018)Shannon Strikes Again! Entropy-based Pruning in Deep Neural Networks for Transfer Learning under Extreme Memory and Computation Budgets., , , , , , and . ICCV (Workshops), page 1510-1514. IEEE, (2023)To update or not to update? Neurons at equilibrium in deep models., , and . NeurIPS, (2022)LOss-Based SensiTivity rEgulaRization: towards deep sparse neural networks., , , and . CoRR, (2020)A round-trip journey in pruned artificial neural networks., , , and . Ital-IA, volume 3486 of CEUR Workshop Proceedings, page 561-566. CEUR-WS.org, (2023)Efficient Inference Of Image-Based Neural Network Models In Reconfigurable Systems With Pruning And Quantization., , , , , , and . ICIP, page 2491-2495. IEEE, (2022)On the Role of Structured Pruning for Neural Network Compression., , , and . ICIP, page 3527-3531. IEEE, (2021)Pruning Artificial Neural Networks: A Way to Find Well-Generalizing, High-Entropy Sharp Minima., , and . ICANN (2), volume 12397 of Lecture Notes in Computer Science, page 67-78. Springer, (2020)