A low-power end-to-end hybrid neuromorphic framework for surveillance applications

22 Oct 2019Andres UssaLuca Della VedovaVandana Reddy PadalaDeepak SinglaJyotibdha AcharyaCharles Zhang LeiGarrick OrchardArindam BasuBharath Ramesh

With the success of deep learning, object recognition systems that can be deployed for real-world applications are becoming commonplace. However, inference that needs to largely take place on the `edge' (not processed on servers), is a highly computational and memory intensive workload, making it intractable for low-power mobile nodes and remote security applications... (read more)

PDF Abstract

Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper

🤖 No Methods Found Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet