Klaus KnoblochAI Research, Lead Principal Engineer Infineon Technologies Dresden
Neuromorphic AI - an Automotive Application View of Event Based Processing
Driven by mobility and internet-of-things, edge-AI has become increasingly important. Whether it is home applications, autonomous driving or fields like sensor networks and drones, all benefit in power and privacy from local AI data processing close to the device or sensor. Applications like autonomous driving or drones doesn’t even allow communication with bigger processing infrastructure due to real-time latency requirements.
However, todays AI algorithms require intensive data processing limiting the scope of edge-AI applications. Although impressive progress has been reached by technology scaling to run GPU, NPU hardware accelerators also on mobile devices, still the power consumption remains a problem to be solved.
Neuromorphic computing, either as in-memory compute or sparse spiking neural networks, will help to overcome these limits of a von Neumann CPU architecture. Spiking networks also allow to include "time", making time-series data processing much more effective. Thus providing solutions beyond static image classification for upcoming challenges in electric and autonomous driving.