Research Overview:
The objective of the project is to develop a set of neuromorphic benchmarks that can be useful to demonstrate the ability for event-based inference hardware. As with conventional ML hardware, there are currently no established benchmarks for neuromorphic hardware. Also, this neuromorphic benchmark must show state-of-the-art performance for edge-relevant applications and run on currently available neuromorphic chips.
In this collaboration with Chromologic, we focus on monocular depth estimation with multi-modal data while targeting the Intel Loihi platform. Accordingly, we are currently working on building and training an event-based network and achieving comparable accuracy as available state-of-the-art works. Once we achieve reasonable accuracy, we will port the relevant part of the network to the Loihi platform and measure its performance compared to conventional GPU implementation.
Research Collaborator:
Chromologic Inc.
