Arquitetura de Hardware Dedicada para CNNs: Uma Análise comparativa entre FP32 e INT8
Resumo
Para viabilizar a implementação de Redes Neurais Convolucionais (CNNs) de alto custo computacional em sistemas restritos, a quantização de modelos, como a conversão de representações de ponto flutuante (float32) para inteiros de 8 bits (int8), é uma técnica essencial. Este trabalho quantifica os ganhos da técnica através da comparação de uma camada convolucional e uma totalmente conectada, descritas em VHDL e sintetizadas para um ASIC de 40 nm. A análise mensura a redução em área e potência da implementação em int8 frente à de float32, validando sua eficácia para hardware de baixo consumo.Referências
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Conceição, R., Porto, M., Peng, W.-H., and Agostini, L. (2025). Cross-platform neural video coding: A case study. In IEEE International Symposium on Circuits and Systems (ISCAS), pages 1–5, Londres, Reino Unido.
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Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems (NeurIPS), volume 25, pages 1097–1105.
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Schroff, F., Kalenichenko, D., and Philbin, J. (2015). Facenet: A unified embedding for face recognition and clustering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 815–823.
Wu, J., Leng, C., Wang, Y., Hu, Q., and Cheng, J. (2016). Quantized convolutional neural networks for mobile devices. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 4820–4828, Las Vegas, NV, USA.
Chen, Y.-H., Krishna, T., Emer, J. S., and Sze, V. (2017). Eyeriss: An energy-efficient reconfigurable accelerator for deep convolutional neural networks. IEEE Journal of Solid-State Circuits, 52(1):127–138.
Conceição, R., Porto, M., Peng, W.-H., and Agostini, L. (2025). Cross-platform neural video coding: A case study. In IEEE International Symposium on Circuits and Systems (ISCAS), pages 1–5, Londres, Reino Unido.
Horowitz, M. (2014). Computing’s energy problem (and what we can do about it). In IEEE International Solid-State Circuits Conference (ISSCC), pages 10–14, San Francisco, CA, USA.
Jacob, B., Kligys, S., Chen, B., Zhu, M., Tang, M., Howard, A., Adam, H., and Kalenichenko, D. (2018). Quantization and training of neural networks for efficient integer-arithmetic-only inference. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems (NeurIPS), volume 25, pages 1097–1105.
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278–2324.
Ren, S., He, K., Girshick, R., and Sun, J. (2017). Faster r-cnn: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6):1137–1149.
Schroff, F., Kalenichenko, D., and Philbin, J. (2015). Facenet: A unified embedding for face recognition and clustering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 815–823.
Wu, J., Leng, C., Wang, Y., Hu, Q., and Cheng, J. (2016). Quantized convolutional neural networks for mobile devices. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 4820–4828, Las Vegas, NV, USA.
Publicado
12/11/2025
Como Citar
ALDRIGHI, Vanessa; MAASS, Denis; CONCEIÇÃO, Ruhan; PORTO, Marcelo; AGOSTINI, Luciano.
Arquitetura de Hardware Dedicada para CNNs: Uma Análise comparativa entre FP32 e INT8. In: ESCOLA REGIONAL DE APRENDIZADO DE MÁQUINA E INTELIGÊNCIA ARTIFICIAL DA REGIÃO SUL (ERAMIA-RS), 1. , 2025, Porto Alegre/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 300-303.
DOI: https://doi.org/10.5753/eramiars.2025.16662.
