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1682

Jang J.-W., Park S., Burr G. W., Hwang H., Jeong Y.-H. (2015). Optimization of conductance change in Pr1−xCaxMnO3-based synaptic devices for neuromorphic systems / IEEE Electron Device Letters, Vol. 36, No. 5, pp. 457—459 // https://researcher.watson.ibm.com/researcher/files/us-gwburr/PCMO_neuromorphic_EDL2015.pdf

1683

Jeong Y. J., Kim S., Lu W. D. (2015). Utilizing multiple state variables to improve the dynamic range of analog switching in a memristor / Applied Physics Letters, Vol. 107 // https://doi.org/10.1063/1.4934818

1684

van de Burgt Y., Lubberman E., Fuller E. J., Keene S. T., Faria G. C., Agarwal S., Marinella M. J., Talin A. A., Salleo A. (2017). A non-volatile organic electrochemical device as a low-voltage artifcial synapse for neuromorphic computing / Nature Materials, Vol. 16, pp. 414—418 // https://doi.org/10.1038/nmat4856

1685

Agarwal S., Jacobs Gedrim R. B., Hsia A. H., Hughart D. R., Fuller E. J., Talin A. A., James C. D., Plimpton S. J., Marinella M. J. (2017). Achieving ideal accuracies in analog neuromorphic computing using periodic carry / 2017 Symposium on VLSI Technology // https://doi.org/10.23919/VLSIT.2017.7998164

1686

Upadhyay N. K., Jiang H., Wang Z., Asapu S., Xia Q., Joshua Yang J. (2019). Emerging Memory Devices for Neuromorphic Computing / Advanced Materials Technologies, 1800589 // https://doi:10.1002/admt.201800589

1687

Oh S., Shi Y., del Valle J., Salev P., Lu Y., Huang Z., Kalcheim Y., Schuller I. K., Kuzum D. (2021). Energy-efficient Mott activation neuron for full-hardware implementation of neural networks / Nature Nanotechnology, Vol. 16, pp. 680—687 // https://doi.org/10.1038/s41565-021-00874-8

1688

Ambrogio S., Narayanan P., Tsai H., Shelby R. M., Boybat I., Nolfo C., Sidler S., Giordano M., Bodini M., Farinha N. C. P., Killeen B., Cheng C., Jaoudi Y., Burr G. W. (2018). Equivalent-accuracy accelerated neural-network training using analogue memory / Nature, Vol. 558, pp. 60—67 // https://doi.org/10.1038/s41586-018-0180-5

1689

Mayberry M. (2017). Intel’s New Self-Learning Chip Promises to Accelerate Artificial Intelligence / Intel newsroom, September 25, 2017 // https://newsroom.intel.com/editorials/intels-new-self-learning-chip-promises-accelerate-artificial-intelligence/

1690

Davies M. (2018). Loihi — a brief introduction // http://niceworkshop.org/wp-content/uploads/2018/05/Mike-Davies-NICE-Loihi-Intro-Talk-2018.pdf

1691

Loihi – Intel / WikiChip // https://en.wikichip.org/wiki/intel/loihi

1692

Mayberry M. (2018). Intel Creates Neuromorphic Research Community to Advance ‘Loihi’ Test Chip / Intel newsroom, March 1, 2018 // https://newsroom.intel.com/editorials/intel-creates-neuromorphic-research-community/

1693

News Byte (2020). Intel Scales Neuromorphic Research System to 100 Million Neurons / Intel newsroom, March 18, 2020 // https://newsroom.intel.com/news/intel-scales-neuromorphic-research-system-100-million-neurons/

1694

Intel Advances Neuromorphic with Loihi 2, New Lava Software Framework and New Partners (2021) / Intel newsroom, September 30, 2021 // https://www.intel.com/content/www/us/en/newsroom/news/intel-unveils-neuromorphic-loihi-2-lava-software.html

1695

Ham D., Park H., Hwang S., Kim K. (2021). Neuromorphic electronics based on copying and pasting the brain / Nature Electronics, Vol. 4, pp. 635—644 // https://doi.org/10.1038/s41928-021-00646-1

1696

Ambrogio S., Narayanan P., Okazaki A., Fasoli A., Mackin C., Hosokawa K., Nomura A., Yasuda T., Chen A., Friz A., Ishii M., Luquin J., Kohda Y., Saulnier N., Brew K., Choi S., Ok I., Philip T., Chan V., Silvestre C., Ahsan I., Narayanan V., Tsai H., Burr G. W. (2023). An analog-AI chip for energy-efficient speech recognition and transcription / Nature, Vol. 620, pp. 768–775 // https://doi.org/10.1038/s41586-023-06337-5

1697

Le Gallo M., Khaddam-Aljameh R., Stanisavljevic M., Vasilopoulos A., Kersting B., Dazzi M., Karunaratne G., Brändli M., Singh A., Müller S. M., Büchel J., Timoneda X., Joshi V., Rasch M. J., Egger U., Garofalo A., Petropoulos A., Antonakopoulos T., Brew K., Choi S., Ok I., Philip T., Chan V., Silvestre C., Ahsan I., Saulnier N., Narayanan V., Francese P. A., Eleftheriou E., Sebastian A. (2023). A 64-core mixed-signal in-memory compute chip based on phase-change memory for deep neural network inference / Nature Electronics, 10 August 2023 // https://doi.org/10.1038/s41928-023-01010-1

1698

Moradi S., Qiao N., Stefanini F., Indiveri G. (2017). A Scalable Multicore Architecture With Heterogeneous Memory Structures for Dynamic Neuromorphic Asynchronous Processors (DYNAPs) / IEEE Transactions on Biomedical Circuits and Systems, Vol. 12, Iss. 1 // https://doi.org/10.1109/TBCAS.2017.2759700

1699

Delbruck T. (2017). The development of the DVS and DAVIS sensors / ICRA 2017 workshop on Event-Based Vision, Singapore, June 2, 2017 // http://rpg.ifi.uzh.ch/docs/ICRA17workshop/Delbruck.pdf

1700

RAMP Technology: Stop wasting battery power on the digitization of irrelevant data / Aspinity // https://www.aspinity.com/Technology

1701

Pei J., Deng L., Song S., Zhao M., Zhang Y., Wu S., Wang G., Zou Z., Wu Z., He W., Chen F., Deng N., Wu S., Wang Y., Wu Y., Yang Z., Ma C., Li G., Han W., Li H., Wu H., Zhao R., Xie Y., Shi L. (2019). Towards artificial general intelligence with hybrid Tianjic chip architecture / Nature, Vol. 572, pp. 106—111 // https://doi.org/10.1038/s41586-019-1424-8

1702

Chen Y., Krishna T., Emer J., Sze V. (2016). Eyeriss: An Energy-Efficient Reconfigurable Accelerator for Deep Convolutional Neural Networks / IEEE ISSCC 2016 // http://eyeriss.mit.edu/

1703

Han S., Liu X., Mao H., Pu J., Pedram A., Horowitz M. A., Dally W. J. (2016). EIE: Efficient Inference Engine on Compressed Deep Neural Network / 2016 ACM/IEEE 43rd Annual International Symposium on Computer Architecture // https://www.cs.virginia.edu/~smk9u/CS6501F16/p243-han.pdf

1704

Нейроморфный процессор «Алтай» (2019) / Мотив: Нейроморфные технологии // https://motivnt.ru/neurochip-altai/

1705

Zhang W., Gao B., Tang J., Yao P., Yu S., Chang M.-F., Yoo H.-J., Qian H., Wu H. (2020). Neuro-inspired computing chips / Nature Electronics, Vol. 3, pp. 371—382 // https://doi.org/10.1038/s41928-020-0435-7

1706

Schneider M. L., Donnelly C. A., Russek S. E., Baek B., Pufall M. R., Hopkins P.

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