Technology enabled computing architectures for efficient neuromorphic engineering

Spotlight talks #2

Modern computation based on von Neumann architecture is now a mature cutting-edge science. In the von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer is responsible for a large part of the power consumption. The next generation computer technology is expected to solve problems at the exascale with 1018 calculations each second. Even though these future computers will be incredibly powerful, if they are based on von Neumann type architectures, they will consume between 20 and 30 megawatts of power and will not have intrinsic physically built-in capabilities to learn or deal with complex data as our brain does. These needs can be addressed by neuromorphic computing systems which are inspired by the biological concepts of the human brain. This new generation of computers has the potential to be used for the storage and processing of large amounts of digital information with much lower power consumption than conventional processors. Among their potential future applications, an important niche is moving the control from data centers to edge devices. The aim of this talk is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of neuromorphic technology, namely materials, devices, neuromorphic circuits, neuromorphic algorithms, applications, and ethics.

Baker Mohammad

Professor

Lunch Break

Event-driven sensing and neuromorphic processing

Event-driven sensing and neuromorphic processing to endow robots with improved energetic and behavioral autonomy .

Baker Mohammad

Professor

Vote of Thanks

Event-driven sensing and neuromorphic processing to endow robots with improved energetic and behavioral autonomy 

Spotlight talks #3

Event-driven sensing enables us to safely interact and control our contacts with our surroundings. Many technical systems and applications could profit from a similar type of sense. Yet, despite the emergence of such systems covering more extensive areas, large-area realizations of system effectively boosting applications are still rare. Recent advancements have improved the deployability and robustness of such systems laying the basis for their scalability. However, the system’s upscaling introduces yet another challenge—the challenge of handling a large amount of heterogeneous information with complex spatial relations between sensing points. This talk targets event-driven approaches for large-area systems and homogenizes the different perspectives on event-driven systems and assesses the applicability of existing event-driven implementations in large-area systems. This will be a guideline for tuning the novelty-threshold of event generators towards realizing a flexible event-driven information handling system on standard computer systems for large-scale system with detailed descriptions on the effective design of event generators and decoders for advanced robotics.

Chiara Bartolozzi

Doctor

Posters session #3

Chiara Bartolozzi

Doctor

Coffee break

Vote of Thanks

Solving Robotic Manipulation with Sparse Reward Reinforcement Learning and Hyperdimensional computing

Spotlight talks #4

Learning with sparse rewards is usually inefficient in Reinforcement Learning (RL). Hindsight Experience Replay (HER) has been shown an effective solution to handle the low sample efficiency that results from sparse rewards by goal relabeling. However, the HER still has an implicit virtual-positive sparse reward problem caused by invariant achieved goals, especially for robot manipulation tasks. To solve this problem, novel model-free continual RL algorithms, such as Relay-HER (RHER) have been proposed. The latter is based on the decomposition and rearrangement of the original long-horizon task into new sub-tasks with incremental complexity. Subsequently, a multi-task networks are designed to learn the sub-tasks in ascending order of complexity. To solve the virtual-positive sparse reward problem, this talk will target Random-Mixed Exploration Strategies, in which the achieved goals of the sub-task with higher complexity are quickly changed under the guidance of the one with lower complexity.

Benjamin Tee

Doctor

Panel discussion

Vote of Thanks

Toward Cognitive Navigation: Design and Implementation of a Biologically Inspired networks

Spotlight talks #5

As a vital cognitive function of animals, the navigation skill is first built on the accurate perception of the directional heading in the environment. Head direction cells (HDCs), found in the limbic system of animals, are proven to play an important role in identifying the directional heading allocentrically in the horizontal plane, independent of the animal’s location and the ambient conditions of the environment. However, practical HDC models that can be implemented in robotic applications are rarely investigated, especially those that are biologically plausible and yet applicable to the real world. This talk will address a computational HDC network that is consistent with several neurophysiological findings concerning biological HDCs and then the concept of their implementation in robotic navigation tasks. The HDC network keeps a representation of the directional heading only relying on the angular velocity as an input for real-world experiments and demonstration of excellent performance in terms of accuracy and real-time capability.

Jorge Dias

Professor

Panel discussion

Closing remarks

Vote of Thanks