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14.5 Eyeriss: An energy-efficient reconfigurable accelerator for deep convolutional neural networks
Open accessAbstract
Deep learning using convolutional neural networks (CNN) gives state-of-the-art accuracy on many computer vision tasks (e.g. object detection, recognition, segmentation). Convolutions account for over 90% of the processing in CNNs for both inference/testing and training, and fully convolutional networks are increasingly being used. To achieve state-of-the-art accuracy requires CNNs with not only a larger number of layers, but also millions of filters weights, and varying shapes (i.e. filter sizes, number of filters, number of channels) as shown in Fig. 14.5.1. For instance, AlexNet [1] uses 2.3 million weights (4.6MB of storage) and requires 666 million MACs per 227×227 image (13kMACs/pixel). VGG16 [2] uses 14.7 million weights (29.4MB of storage) and requires 15.3 billion MACs per 224×224 image (306kMACs/pixel). The large number of filter weights and channels results in substantial data movement, which consumes significant energy.
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Cited by
Efficient Processing of Deep Neural Networks: A Tutorial and Survey
Sze, V., Chen, Y.-H., Yang, T.-J., & Emer, J. S. (2017) · Proceedings of the IEEE · doi:10.1109/jproc.2017.2761740 · Cited by 4,215
Self-citationEyeriss: An Energy-Efficient Reconfigurable Accelerator for Deep Convolutional Neural Networks
Chen, Y.-H., Krishna, T., Emer, J. S., & Sze, V. (2017) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2016.2616357 · Cited by 3,247
Self-citationEyeriss: a spatial architecture for energy-efficient dataflow for convolutional neural networks
Chen, Y.-H., Emer, J., & Sze, V. (2016) · ACM SIGARCH Computer Architecture News · doi:10.1145/3007787.3001177 · Cited by 1,182
Self-citationNeuro-Inspired Computing With Emerging Nonvolatile Memorys
Yu, S. (2018) · Proceedings of the IEEE · doi:10.1109/jproc.2018.2790840 · Cited by 1,170
Neuro-inspired computing chips
Zhang, W., Gao, B., Tang, J., Yao, P., Yu, S., Chang, M.-F., Yoo, H.-J., Qian, H., & Wu, H. (2020) · Nature Electronics · doi:10.1038/s41928-020-0435-7 · Cited by 892
Metadata
- Authors
- Chen, Y.-H., Krishna, T., Emer, J., & Sze, V.
- Venue
- 2016 IEEE International Solid-State Circuits Conference (ISSCC)
- Publisher
- IEEE
- Published
- 2016-01
- Pages
- 262-263
- ISSN
- 0193-6530 (ISSN-L)
- OpenAlex
- W3023154643
- Cited by
- 713
- References
- 0
- FWCI
- 37.00
- Type
- conference paper
- Language
- en
- Open access
- green
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- Chen, Y.-H., Krishna, T., Emer, J., & Sze, V. (2016). 14.5 Eyeriss: An energy-efficient reconfigurable accelerator for deep convolutional neural networks. 2016 IEEE International Solid-State Circuits Conference (ISSCC). https://doi.org/10.1109/isscc.2016.7418007
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