Deep Learning Projects

We are listing a few projects below to give you an idea of what we do. However, we constantly have new project ideas and maybe some other approaches become obsolete in the very rapidly advancing research area. Please just contact the people of a project most similar to what you would like to do, and come talk to us.

Prerequisites

We have no strict, general requirements, as they are highly dependent on the exact project steps. The projects will be adapted to the skills and interests of the student(s) -- just come talk to us! If you don't know about GPU programming or CNNs or ... just let us know and we can together determine what is a useful way to go -- after all you are here to learn not only about project work, but also to develop your technical skills.

Only hard requirements:

• Excitement for deep learning
• For VLSI projects: VLSI 1 or equivalent

Projects

Project Name	Description	Platform	Workload Type	Contact(s)
CBinfer+	Running CNNs to a large extent means a tremendous computational effort, particularly when looking at object detection and image segmentation. For many applications we want to run everything in real-time on video data. We exploit that only few pixels change from frame to frame (cf. paper). We have several ideas on a) how to improve the algorithm, and b) would like to optimize the implementation for the next generation of (embedded) GPUs. You can be part of this (already successful) research path.	Embedded GPU (Tegra X2)	SW (GPU, algo evals)	Lukas Cavigelli
CBinfer HW	The motiviation here is the same as for CBinfer+. Here we want to build a custom hardware architecture for ASICs capable of running this type of workload.	ASIC	HW (ASIC)	Lukas Cavigelli
Distributed/Federated Learning	With the increasing number of IoT devices equipped with a bunch of sensor, it is not feasible to always stream all the data back to a server. Therefore, there is the need to learn on the node itself and synchronize/merge the network in a periodic scheme.	Embedded GPU	SW(algo, evals)	Renzo Andri, Lukas Cavigelli
The Always-On Self-Learning Camera Node	Example	Example	Example
Self-Learning Drone	Autonomous Driving is a hot topic nowadays, but also self-learning approaches (i.e. re-inforcement learning) have had a big success (e.g. AlphaGo from Google beat the world champion in Go. We want a drone to learn from its environment such that the drone is able to solve a task independantly.	ML frameworks (e.g. Torch)/GPU, Drone Simulation (ROS/Gazebo)	SW (Training)	Renzo Andri, Daniele Palossi
On-chip learning	Neural Networks are compute and resource intensive and are usually run on power-intensive GPU clusters, but we would like to exploit them also on the everywhere IoT devices. To reach that we need to develop new hardware architecture optimized for this application. This also include to check new algorithmic approach, which can reduce the compute or memory footprint of these networks.	ASIC	HW (ASIC)	Renzo Andri

Workload types: SW (GPU), SW (microcontr.), SW (algorithm evals), HW (FPGA), HW (ASIC), HW (PCB)