• Full day event workshop proposal.
    Embodied Neuromorphic AI for Robotic Perception
    May,29, 2023 - London

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  • Location

    London
  • Tickets

    tickets remaining
  • Speakers

    5 professional Speakers

Overview

The design of robots that interact autonomously with the environment and exhibit complex behaviors is an open challenge that can benefit from understanding what makes living beings fit to act in the world. Neuromorphic engineering studies neural computational principles to develop technologies that can provide a computing substrate for building compact and low-power processing systems. In this workshop we aim to discuss why endowing robots with neuromorphic technologies – from perception to motor control – represents a promising approach for the creation of robots which can seamlessly integrate in society. Highlighting open challenges in this direction, we propose community participations and actions required to overcome current limitations.

Event Schedule

Robotic perception challenges and neuromorphic computing opportunities
Neuromorphic computing opportunities

Neuromorphic systems bring a paradigm shift in the way computation is performed in robotics. Traditional computing and deep learning supported the recent progress of robotics, but lack robustness and adaptation to bring robots operating in ever-changing scenarios and tasks. Neuromorphic intelligence develops brain-inspired supporting technology featuring brain-like adaptation, robustness and efficiency. These systems are based on event-driven (change detection) sensing, spike-based cooperative and competitive computation for perception and for control. This talk discusses state-of-the-art neuromorphic technologies for event-driven sensing and briefly explains how they operate differently to standard sensors. It then describes different frameworks for developing perception modules and neuromorphic computing platforms supporting some of those. The perception modules can cooperate in a closed loop with the actuation system to directly control motors with the output spikes of the networks. The high potential for solving current robotic problems leaves a strong outlook for the field of neuromorphic robots in the coming years.

Alois Christian Knoll
Professor
  • 09:15 - 10:25
  • Spotlight talks #1
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Posters session #1
Alois Christian Knoll
Professor
  • 10:25 - 10:45
  • Posters session #1
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Coffee Break
Posters session #2
Alois Christian Knoll
Professor
  • 10:45 - 11:00
  • Posters session #1
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Vote of Thanks
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
  • 11:00 – 12:00
  • Convention Center
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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
  • 13:45 - 14:45
  • Convention Center
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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
  • 14:45 – 15:15
  • Convention Center
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Posters session #3
Chiara Bartolozzi
Doctor
  • 15:15- 16:00
  • Convention Center
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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
  • 16:30 – 17:00
  • Convention Center
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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
  • 16:30 – 17:00
  • Convention Center
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Panel discussion
Closing remarks
Vote of Thanks

Our Speakers


Baker Mohammad

Baker Mohammad received the B.S. degree from the University of New Mexico, Albuquerque, the M.S. degree from Arizona State University, Tempe, and the Ph.D. degree from The University of Texas at Austin, in 2008, all in ECE.

He is currently the Director of the System on Chip Center and an Associate Professor in EECS at Khalifa University. Prior to joining Khalifa University, he was a Senior Staff Engineer/the Manager at Qualcomm, Austin, USA, for six years, where he was engaged in designing high-performance and low-power DSP processors used for communication and multimedia applications. Before joining Qualcomm, he worked for ten years at Intel Corporation on a wide range of microprocessors design from high-performance, server chips more than 100Watt (IA-64), to mobile embedded processor low power sub one watt (xscale). He has over 16 years of industrial experience in microprocessor design, emphasizing memory, low power circuits, and physical design. His research interests include VLSI, power-efficient computing, high yield embedded memory, emerging technologies, such as memristor, STTRAM, in-memory-computing, and hardware accelerators for cyber-physical systems. He is also engaged in a microwatt-range computing platform for wearable electronics and WSN, focusing on energy harvesting, power management, and power conversion, including efficient DC/DC and AC/DC converters. He authored/coauthored over 150 refereed journals and conference proceedings, more than three books, more than 18 U.S. patents, multiple invited seminars/panelists, and the presenter of more than three conference tutorials, including one tutorial on energy harvesting and power management for WSN at the 2015 (ISCAS). He is an Associate Editor of IEEE Access and IEEE Transaction on Very Large Scale Integration (VLSI) Systems.

Address:

UAE, P.O. Box: 127788
Abu Dhabi

Contact:

Phone: +971 2 312 4499
Email: baker.mohammad@ku.ac.ae

Benjamin Tee

Dr. Benjamin C.K. Tee is appointed President’s Assistant Professor in Materials Science and Engineering Department at the National University of Singapore (NUS). He obtained his PhD (EE) at Stanford University, and was a Singapore-Stanford Biodesign Global Innovation Postdoctoral Fellow in 2014. He has developed and patented several award-winning electronic skin sensor technologies.

He is an MIT TR35 Innovator (Global) in 2015 and Singapore National Research Foundation (NRF) Fellow. His research group aims to develop technologies at the intersection of materials science, mechanics, electronics and biology, with a focus on sensitive electronic skins that has tremendous potential to advance global healthcare technologies in an increasingly Artificial Intelligence (AI) and Robotics driven era. He can be reached at www.benjamintee.com.


Address:

xxxxxxx
xxx

Contact:

Phone: +xxx xxx xxx
Email: benjamin.tee@nus.edu.sg

Chiara Bartolozzi

Chiara Bartolozzi is Researcher at the Italian Institute of Technology. She earned a degree in Engineering at University of Genova (Italy) and a Ph.D. in Neuroinformatics at ETH Zurich

developing analog subthreshold circuits for emulating biophysical neuronal properties onto silicon and modelling selective attention on hierarchical multi-chip systems. She is currently leading the Event-Driven Perception for Robotics group, with the aim of applying the “neuromorphic” engineering approach to the design of robotic platforms as enabling technology towards the design of autonomous machines. Chiara has participated to a number of EU funded projects, she is currently coordinating the European Training Network “NeuTouch”, where 15 PhD students are studying how touch perception works in humans and animals, in order to develop artificial touch perception systems for robots and hand prosthesis. As leader of the educational activities of the coordination and support action NEUROTECH, she is co-organising the Neuromorphic Colloquium, a series of online events to build up educational material for the next generation of neuromorphic researchers. She is an IEEE member, actively supporting the CAS and RAS societies. In 2020, she has co-chaired “AICAS2020”, on Circuits and systems for efficient embedded AI.


Address:

via San Quirico 19D, 16163 Genova.
Italy

Contact:

Phone:+39 010 71781 474
Email: chiara.bartolozzi@iit.it

Habil. Alois Christian Knoll

Alois C. Knoll is a computer scientist and professor at Technical University Munich TUM. He teaches and conducts research in the fields of autonomous and embedded systems, robotics and artificial intelligence.

In 2011, he founded the interdisciplinary course "Robotics, Cognition, and Intelligence" at TUM, which has become one of the largest MSc programs in TUM’s CS Dept. In 2007 he became a member of the EU's highest advisory board for information technology, ISTAG. In this function, he was in involved in the development of the EU flagship projects and was ab author of the report "European Challenges and Flagships 2020". In 2009, he co-founded "fortiss", the Munich Institute for Software, which has since been transformed into a state institute of Bavaria. He has coordinated the project ECHORD++, an initiative to bring together the robotics industry and universities to speed up robot technology’s route to market. He has been head of the sub-project "Neurorobotics" of the EU flagship project “Human Brain Project”. Since 2011 he has been PI at TUMCREATE, a joint venture of NTU and TUM-Asia in Singapore. His focus there is on modeling, simulation and optimization for infrastructure, both in methodical development and in the construction of practical systems.


Address:

Technical University of Munich.
Germany

Contact:

Phone:+49 (89) 289 – 18104
Email:knoll@mytum.de

Joel Emer

Joel Emer is a Professor of the Practice at MIT's Electrical Engineering and Computer Science Department (EECS) and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL). He is also a Senior Distinguished Research Scientist at Nvidia in Westford, MA, where he is responsible for exploration of future architectures as well as modeling and analysis methodologies. Prior to joining NVIDIA, he worked at Intel where he was an Intel Fellow and Director of Microarchitecture Research. Previously he worked at Compaq and Digital Equipment Corporation (DEC) .

Dr. Emer has held various research and advanced development positions investigating processor micro-architecture and developing performance modeling and evaluation techniques. He has made architectural contributions to a number of VAX, Alpha and X86 processors and is recognized as one of the developers of the widely employed quantitative approach to processor performance evaluation. He has also been recognized for his contributions in the advancement of simultaneous multi-threading technology, analysis of the architectural impact of soft errors, memory dependence prediction, pipeline and cache organization, performance modeling methodologies and spatial architectures.


Address:

xxx
xx

Contact:

Phone: +49 258-9190
Email: emer@csail.mit.edu


Knowledgable speakers

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25 Awesome Sessions

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Picture perfect setting

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Call for Contributions

⦁ Call for the neuromorphic community

To favor the uptake and the building of a larger community of users and stakeholders of embodied neuromorphic intelligence, the neuromorphic community should focus on the design of modular and reusable sensing and computing modules. The standardization of a common communication protocol, has already enabled sharing of modules and systems. Open-discussions and open-source implementations of algorithms and dataset-sharing will promote the growth of the field. A milestone on this path will be the definition of a suite of benchmarks that can be used to quantitatively compare the features and benefits of different neuromorphic systems.

⦁ Call for the computational neuroscience community

Neuromorphic circuits need to convert sensory signals into address-events for further processing. The computational neuroscience community has a unique opportunity to inspire and educate neuromorphic engineers by pointing out the principles and strategies that the nervous system uses to convert analogue inputs to spikes and encode sensory signals. Tight collaboration with the neuroscience community will lead to important improvements in neuromorphic sensing circuits. Similarly, this community can provide useful insights for designing recurrent Spiking Neural Networks (SNNs) composed of noisy and inhomogeneous circuits to carry out signal processing and computation. In this respect, it will be important to link specific neuroscience observations to their most basic computational role in order to isolate the basic mechanisms that are sufficient to implement a given functionality. The hardware implementation will then reproduce such a reduced “minimalist” model, where features, complexity, detail, and diversity have corresponding computational functions.

⦁ Call for the material science community

Emerging memory technologies, like memristors hold great promises for improving conventional computing architectures, However, they also represent an important opportunity for designing new types of solid-state nano-scale devices that could directly emulate the physics of real synapses, and therefore provide the computing substrate for implementing the principles of neural computation more efficiently. The material science community should therefore attempt to embrace and exploit the non-linear physics of these devices to optimize the design of embodied neuromorphic computing architectures.

⦁ Call for the computer science community

Similar to how computers use a hierarchy of levels of abstraction to manage the definition of complex operations, computer science can leverage on the notions and tools developed so far to define new methods for combining neural computational primitives, to achieve intelligent functionalities. A challenge that lays ahead is also how to formalize computation using non-linear dynamics, stochastic, and probabilistic methods, including embodiment in the robotic platform.

⦁ Call for the soft robotics community

As the neuromorphic approach is a good fit for complex systems where the control is non-trivial, it is a perfect match to soft robotics. There is a need for undefined, providing use cases to the neuromorphic community. The resulting perceptive and cognitive functions–implemented using neuromorphic computational substrates–must be embedded on robots, where the morphology of the platform can influence the way sensory signals are acquired (e.g. through a different placement of the sensors) and the way actions are executed (e.g. different kinds of locomotion, rigid versus soft actuation, etc.). Neuromorphic engineering, thanks to its ability to implement adaptive circuits and systems for solving non-linear control systems, can offer a solution to the complex control of soft robots.

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