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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.