Reviews (relevant to all videos)¶
- Frenkel et al. (2023) “Bottom-up and top-down approaches for the design of neuromorphic processing systems: Tradeoffs and synergies between natural and artificial intelligence”
- Schuman et al. (2017) “A Survey of Neuromorphic Computing and Neural Networks in Hardware”
- Indiveri and Liu (2015) “Memory and information processing in neuromorphic systems”
- Christensen et al. (2022) “2022 roadmap on neuromorphic computing and engineering”
Introduction¶
Computing¶
Sensing¶
- Christensen, D. V., Dittmann, R., Linares-Barranco, B., Sebastian, A., Le Gallo, M., Redaelli, A., Slesazeck, S., Mikolajick, T., Spiga, S., Menzel, S., Valov, I., Milano, G., Ricciardi, C., Liang, S.-J., Miao, F., Lanza, M., Quill, T. J., Keene, S. T., Salleo, A., … Pryds, N. (2022). 2022 roadmap on neuromorphic computing and engineering. Neuromorphic Computing and Engineering, 2(2), 022501. 10.1088/2634-4386/ac4a83
- Mittal, S. (2016). A Survey of Techniques for Approximate Computing. ACM Computing Surveys, 48(4), 1–33. 10.1145/2893356
- Bartolozzi, C., & Indiveri, G. (2007). Synaptic Dynamics in Analog VLSI. Neural Computation, 19(10), 2581–2603. 10.1162/neco.2007.19.10.2581
- Vanarse, A., Osseiran, A., & Rassau, A. (2016). A Review of Current Neuromorphic Approaches for Vision, Auditory, and Olfactory Sensors. Frontiers in Neuroscience, 10. 10.3389/fnins.2016.00115
- Schuman, C., Patton, R., Kulkarni, S., Parsa, M., Stahl, C., Haas, N. Q., Mitchell, J. P., Snyder, S., Nagle, A., Shanafield, A., & Potok, T. (2022). Evolutionary vs imitation learning for neuromorphic control at the edge*. Neuromorphic Computing and Engineering, 2(1), 014002. 10.1088/2634-4386/ac45e7