GPAM: Genetic Programming with Associative Memory

Author:

Juza Tadeas,Sekanina LukasORCID

Publisher

Springer Nature Switzerland

Reference18 articles.

1. Capra, M., Bussolino, B., Marchisio, A., Shafique, M., Masera, G., Martina, M.: An updated survey of efficient hardware architectures for accelerating deep convolutional neural networks. Future Internet 12(7), 113 (2020)

2. Dupuis, E., Novo, D., O’Connor, I., Bosio, A.: A heuristic exploration of retraining-free weight-sharing for CNN compression. In: 27th Asia and South Pacific Design Automation Conference, ASP-DAC, pp. 134–139. IEEE (2022)

3. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)

4. Han, S., Mao, H., Dally, W.J.: Deep compression: compressing deep neural network with pruning, trained quantization and Huffman coding. In: 4th International Conference on Learning Representations, ICLR (2016)

5. Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)

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