NEUROTECH event: Future Application Directions for Neuromorphic Computing Technologies: agenda and registration (free, but mandatory). A half-day event with special focus on potential application of neuromorphic computing.
Getting to the venue:
the nearest tram stop to the meeting venue is "Heidelberg Bunsengymnasium" (marked in the map linked above) [online timetable]https://reiseauskunft.bahn.de//bin/query.exe/en?Z=Neuenheim+Bunsengymnasium,+Heidelberg), provided by German Railway. Here you can also buy tickets online
Lightning talk: Neuromorphic Computing for Spacecraft’s Terrain Relative Navigation: A Case of Event-Based Crater Classification Task
Kazuki Kariya and Seisuke Fukuda
Terrain relative navigation is a key technology to enhance conventional spacecraft navigation systems for accurate landing on a planetary body. Since the navigation task is self-localization based on terrain information, computer vision tasks using terrain images are often used for feature extraction and matching. Although the navigation system requires real-time and onboard processing capability due to high-speed descent and the communication propagation delay, the processing performance of space-grade computers is about two orders of magnitude less than commercial ones. This decline in the performance is caused by the power constraints and the acquisition of radiation hardening inherent in the space environments. Neuromorphic computing architecture may meet this need in terms of power consumption and processing speed.
In this study, we investigate the applicability of neuromorphic computing systems for a crater classification as a function of terrain relative navigation. The navigation system consists of a spiking neural network that processes the classification task and an event-based camera that provides terrain information as input to the network. Results show that the system can classify craters with very low power consumption while maintaining performance comparable to existing computing architectures.
Kazuki Kariya (The Graduate University for Advanced Studies, SOKENDAI)
15:55-16:25 (30 min)
16:25-16:45 (20+5 min)
Beyond Backprop: Different Approaches to Credit Assignment in Neural Nets
Intel Loihi platform tutorial (Lecture style. To follow along from your own laptop your need to engage with Intel’s Intel’s Neuromorphic Research Community beforehand (email email@example.com for more information).