LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).
Krizhevsky, A., Sutskever, I. & Hinton, G. E. ImageNet classification with deep convolutional neural networks. Commun. ACM 60, 84–90 (2017).
He, K., Zhang, X., Ren, S. & Sun, J. in Proc. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 770–778 (IEEE, 2016).
Vinyals, O. et al. Grandmaster level in StarCraft II using multi-agent reinforcement learning. Nature 575, 350–354 (2019).
Shen, Y. et al. Deep learning with coherent nanophotonic circuits. Nat. Photonics 11, 441–446 (2017).
Lin, X. et al. All-optical machine learning using diffractive deep neural networks. Science 361, 1004–1008 (2018).
Hamerly, R., Bernstei, L., Sludds, A., Soljačić, M. & Englund, D. Large-scale optical neural networks based on photoelectric multiplication. Phys. Rev. X 9, 021032 (2019).
Feldmann, J. et al. Parallel convolutional processing using an integrated photonic tensor core. Nature 589, 52–58 (2021).
Dong, B. et al. Partial coherence enhances parallelized photonic computing. Nature 632, 55–62 (2024).
Dong, B. et al. Higher-dimensional processing using a photonic tensor core with continuous-time data. Nat. Photonics 17, 1080–1088 (2023).
Becker, S., Englund, D. & Stiller, B. An optoacoustic field-programmable perceptron for recurrent neural networks. Nat. Commun. 15, 3020 (2024).
Chen, Z. et al. Deep learning with coherent VCSEL neural networks. Nat. Photonics 17, 723–730 (2023).
Wang, T. et al. An optical neural network using less than 1 photon per multiplication. Nat. Commun. 13, 123 (2022).
Sludds, A. et al. Delocalized photonic deep learning on the internet’s edge. Science 378, 270–276 (2022).
Shalf, J. The future of computing beyond Moore’s Law. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 378, 20190061 (2020).
Schwierz, F. & Liou, J. J. in Proc. 2020 IEEE Latin America Electron Devices Conference (LAEDC) 1–4 (IEEE, 2020).
Leiserson, C. E. et al. There’s plenty of room at the Top: what will drive computer performance after Moore’s law? Science 368, eaam9744 (2020).
Moore, G. E. Cramming more components onto integrated circuits. Proc. IEEE 86, 82–85 (1998).
Waldrop, M. M. The chips are down for Moore’s law. Nature 530, 144–147 (2016).
Vaswani, A. et al. in Proc. Advances in Neural Information Processing Systems 30 (eds Guyon, I. et al.) 5998–6008 (Curran Associates, 2017).
Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. in Proc. 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) (eds Burstein, J., Doran, C. & Solorio, T.) 4171–4186 (Association for Computational Linguistics, 2019).
Mnih, V. et al. Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015).
Jouppi, N. P. et al. in Proc. 44th Annual International Symposium on Computer Architecture (ISCA ’17) 1–12 (ACM, 2017).
Peng, B., Hua, S., Su, Z., Xu, Y. & Shen, Y. in Proc. 2022 IEEE Photonics Conference (IPC) (IEEE, 2022).
Youngblood, N. Coherent photonic crossbar arrays for large-scale matrix-matrix multiplication. IEEE J. Sel. Top. Quantum Electron. 29, 1–11 (2023).
Zhang, H. et al. An optical neural chip for implementing complex-valued neural network. Nat. Commun. 12, 457 (2021).
Wetzstein, G. et al. Inference in artificial intelligence with deep optics and photonics. Nature 588, 39–47 (2020).
Demirkiran, C. et al. An electro-photonic system for accelerating deep neural networks. ACM J. Emerg. Technol. Comput. Syst. 19, 1–31 (2023).
Pintus, P. et al. Integrated non-reciprocal magneto-optics with ultra-high endurance for photonic in-memory computing. Nat. Photonics 19, 54–62 (2025).
Shastri, B. J. et al. Photonics for artificial intelligence and neuromorphic computing. Nat. Photonics 15, 102–114 (2021).
Xu, X. et al. 11 TOPS photonic convolutional accelerator for optical neural networks. Nature 589, 44–51 (2021).
Jacob, B. et al. in Proc. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2704–2713 (IEEE, 2018).
Courbariaux, M., Bengio, Y. & David, J.-P. Training deep neural networks with low precision multiplications. Preprint at https://arxiv.org/abs/1412.7024 (2015).
Kirtas, M. et al. Mixed-precision quantization-aware training for photonic neural networks. Neural Comput. Appl. 35, 21361–21379 (2023).
Basumallik, A. et al. Adaptive block floating-point for analog deep learning hardware. Preprint at https://arxiv.org/abs/2205.06287 (2022).
Giewont, K. et al. 300-mm monolithic silicon photonics foundry technology. IEEE J. Sel. Top. Quantum Electron. 25, 1–11 (2019).
Ghafarian, H. et al. A 9-bit, 45 mW, 0.05 mm2 source-series-terminated DAC driver with echo canceller in 22-nm CMOS for in-vehicle communication. IEEE Solid-State Circuits Lett. 4, 10–13 (2021).
Yu, K. et al. in Proc. 2015 IEEE International Solid-State Circuits Conference – (ISSCC) Digest of Technical Papers https://doi.org/10.1109/isscc.2015.7063098 (IEEE, 2015).
McCreary, J. L. & Gray, P. R. All-MOS charge redistribution analog-to-digital conversion techniques. I. IEEE J. Solid-State Circuits 10, 371–379 (1975).
Jang, M. et al. Design techniques for energy-efficient analog-to-digital converters. IEEE Open J. Solid-State Circuits Soc. 3, 145–161 (2023).
Ramkaj, A. T. et al. A 5-GS/s 158.6-mW 9.4-ENOB passive-sampling time-interleaved three-stage pipelined-SAR ADC with analog-digital corrections in 28-nm CMOS. IEEE J. Solid-State Circuits 55, 1553–1564 (2020).
Lagos, J. et al. A 10.1-ENOB, 6.2-fJ/conv.-step, 500-MS/s, ringamp-based pipelined-SAR ADC with background calibration and dynamic reference regulation in 16-nm CMOS. IEEE J. Solid-State Circuits 57, 1112–1124 (2022).
de Lima, T. F. et al. Noise analysis of photonic modulator neurons. IEEE J. Sel. Top. Quantum Electron. 26, 1–9 (2020).
Karpathy, A. nanoGPT. GitHub https://github.com/karpathy/nanoGPT (2023).
Wang, C. et al. Integrated lithium niobate electro-optic modulators operating at CMOS-compatible voltages. Nature 562, 101–104 (2018).
Abel, S. et al. Large Pockels effect in micro- and nanostructured barium titanate integrated on silicon. Nat. Mater. 18, 42–47 (2019).
Youngblood, N., Chen, C., Koester, S. J. & Li, M. Waveguide-integrated black phosphorus photodetector with high responsivity and low dark current. Nat. Photonics 9, 247–252 (2015).
Parkhi, O. M., Vedaldi, A., Zisserman, A. & Jawahar, C. V. in Proc. 2012 IEEE Conference on Computer Vision and Pattern Recognition 3498–3505 (IEEE, 2012).
