Event-Driven Technologies for Reactive Motion Planning: Neuromorphic Stereo Vision and Robot Path Planning and Their Application on Parallel Hardware

Die Robotik wird immer mehr zu einem Schlüsselfaktor des technischen Aufschwungs. Trotz beeindruckender Fortschritte in den letzten Jahrzehnten, übertreffen Gehirne von Säugetieren in den Bereichen Sehen und Bewegungsplanung noch immer selbst die leistungsfähigsten Maschinen. Industrieroboter sind sehr schnell und präzise, aber ihre Planungsalgorithmen sind in hochdynamischen Umgebungen, wie sie für die Mensch-Roboter-Kollaboration (MRK) erforderlich sind, nicht leistungsfähig genug. Ohne schnelle und adaptive Bewegungsplanung kann sichere MRK nicht garantiert werden. Neuromorphe Technologien, einschließlich visueller Sensoren und Hardware-Chips, arbeiten asynchron und verarbeiten so raum-zeitliche Informationen sehr effizient. Insbesondere ereignisbasierte visuelle Sensoren sind konventionellen, synchronen Kameras bei vielen Anwendungen bereits überlegen. Daher haben ereignisbasierte Methoden ein großes Potenzial, schnellere und energieeffizientere Algorithmen zur Bewegungssteuerung in der MRK zu ermöglichen. In dieser Arbeit wird ein Ansatz zur flexiblen reaktiven Bewegungssteuerung eines Roboterarms vorgestellt. Dabei wird die Exterozeption durch ereignisbasiertes Stereosehen erreicht und die Pfadplanung ist in einer neuronalen Repräsentation des Konfigurationsraums implementiert. Die Multiview-3D-Rekonstruktion wird durch eine qualitative Analyse in Simulation evaluiert und auf ein Stereo-System ereignisbasierter Kameras übertragen. Zur Evaluierung der reaktiven kollisionsfreien Online-Planung wird ein Demonstrator mit einem industriellen Roboter genutzt. Dieser wird auch für eine vergleichende Studie zu sample-basierten Planern verwendet. Ergänzt wird dies durch einen Benchmark von parallelen Hardwarelösungen wozu als Testszenario Bahnplanung in der Robotik gewählt wurde. Die Ergebnisse zeigen, dass die vorgeschlagenen neuronalen Lösungen einen effektiven Weg zur Realisierung einer Robotersteuerung für dynamische Szenarien darstellen. Diese Arbeit schafft eine Grundlage für neuronale Lösungen bei adaptiven Fertigungsprozesse, auch in Zusammenarbeit mit dem Menschen, ohne Einbußen bei Geschwindigkeit und Sicherheit. Damit ebnet sie den Weg für die Integration von dem Gehirn nachempfundener Hardware und Algorithmen in die Industrierobotik und MRK

neuroAIx-Framework: design of future neuroscience simulation systems exhibiting execution of the cortical microcircuit model 20× faster than biological real-time

IntroductionResearch in the field of computational neuroscience relies on highly capable simulation platforms. With real-time capabilities surpassed for established models like the cortical microcircuit, it is time to conceive next-generation systems: neuroscience simulators providing significant acceleration, even for larger networks with natural density, biologically plausible multi-compartment models and the modeling of long-term and structural plasticity.MethodsStressing the need for agility to adapt to new concepts or findings in the domain of neuroscience, we have developed the neuroAIx-Framework consisting of an empirical modeling tool, a virtual prototype, and a cluster of FPGA boards. This framework is designed to support and accelerate the continuous development of such platforms driven by new insights in neuroscience.ResultsBased on design space explorations using this framework, we devised and realized an FPGA cluster consisting of 35 NetFPGA SUME boards.DiscussionThis system functions as an evaluation platform for our framework. At the same time, it resulted in a fully deterministic neuroscience simulation system surpassing the state of the art in both performance and energy efficiency. It is capable of simulating the microcircuit with 20× acceleration compared to biological real-time and achieves an energy efficiency of 48nJ per synaptic event

A Scalable Approach to Modeling on Accelerated Neuromorphic Hardware.

Neuromorphic systems open up opportunities to enlarge the explorative space for computational research. However, it is often challenging to unite efficiency and usability. This work presents the software aspects of this endeavor for the BrainScaleS-2 system, a hybrid accelerated neuromorphic hardware architecture based on physical modeling. We introduce key aspects of the BrainScaleS-2 Operating System: experiment workflow, API layering, software design, and platform operation. We present use cases to discuss and derive requirements for the software and showcase the implementation. The focus lies on novel system and software features such as multi-compartmental neurons, fast re-configuration for hardware-in-the-loop training, applications for the embedded processors, the non-spiking operation mode, interactive platform access, and sustainable hardware/software co-development. Finally, we discuss further developments in terms of hardware scale-up, system usability, and efficiency

Communication sparsity in distributed spiking neural network simulations to improve scalability

In the last decade there has been a surge in the number of big science projects interested in achieving a comprehensive understanding of the functions of the brain, using Spiking Neuronal Network (SNN) simulations to aid discovery and experimentation. Such an approach increases the computational demands on SNN simulators: if natural scale brain-size simulations are to be realized, it is necessary to use parallel and distributed models of computing. Communication is recognized as the dominant part of distributed SNN simulations. As the number of computational nodes increases, the proportion of time the simulation spends in useful computing (computational efficiency) is reduced and therefore applies a limit to scalability. This work targets the three phases of communication to improve overall computational efficiency in distributed simulations: implicit synchronization, process handshake and data exchange. We introduce a connectivity-aware allocation of neurons to compute nodes by modeling the SNN as a hypergraph. Partitioning the hypergraph to reduce interprocess communication increases the sparsity of the communication graph. We propose dynamic sparse exchange as an improvement over simple point-to-point exchange on sparse communications. Results show a combined gain when using hypergraph-based allocation and dynamic sparse communication, increasing computational efficiency by up to 40.8 percentage points and reducing simulation time by up to 73%. The findings are applicable to other distributed complex system simulations in which communication is modeled as a graph network

JTIT

kwartalni

Improving scalability of large-scale distributed Spiking Neural Network simulations on High Performance Computing systems using novel architecture-aware streaming hypergraph partitioning

After theory and experimentation, modelling and simulation is regarded as the third pillar of science, helping scientists to further their understanding of a complex system. In recent years there has been a growing scientific focus on computational neuroscience as a means to understand the brain and its functions, with large international projects (Human Brain Project, Brain Activity Map, MindScope and \textit{China Brain Project}) aiming to further our knowledge of high level cognitive functions. They are a testament to the enormous interest, difficulty and importance of solving the mysteries of the brain. Spiking Neural Network (SNN) simulations are widely used in the domain to facilitate experimentation. Scaling SNN simulations to large networks usually results in more-than-linear increase in computational complexity. The computing resources required at the brain scale simulation far surpass the capabilities of personal computers today. If those demands are to be met, distributed computation models need to be adopted, since there is a slow down of improvements in individual processors speed due to physical limitations on heat dissipation. This is a significant change that requires careful management of the workload in many levels: partition of work, communication and workload balancing, efficient inter-process communication and efficient use of available memory. If large scale neuronal network models are to be run successfully, simulators must consider these, and offer a viable solution to the challenges they pose. Large scale SNN simulations evidence most of the issues of general HPC systems evident in large distributed computation. Commonly used distribution of workload algorithms (round robin, random and manual allocation) do not take into consideration connectivity locality, which is natural in biological networks, which can lead to increased communication requirements when distributing the simulation in multiple computing nodes. State-of-the-art SNN simulations use dense communication collectives to distribute spike data. The common method of point to point communication in distributed computation is through dense patterns. Sparse communication collectives have been suggested to incur in lower overheads when the application's pattern of communication is sparse. In this work we characterise the bottlenecks on communication-bound SNN simulations and identify communication balance and sparsity as the main contributors to scalability. We propose hypergraph partitioning to distribute neurons along computing nodes to minimise communication (increasing sparsity). A hypergraph is a generalisation of graphs, where a (hyper)edge can link 2 or more vertices at once. Coupled with a novel use of sparse-aware communication collective, computational efficiency increases by up to 40.8 percent points and simulation time reduces by up to 73\%, compared to the common round-robin allocation in neuronal simulators. HPC systems have, by design, highly hierarchical communication network links, with qualitative differences in communication speed and latency between computing nodes. This can create a mismatch between the distributed simulation communication patterns and the physical capabilities of the hardware. If large distributed simulations are to take full advantage of these systems, the communication properties of the HPC need to be taken into consideration when allocating workload to route frequent, heavy communication through fast network links. Strategies that consider the heterogeneous physical communication capabilities are called architecture-aware. After demonstrating that hypergraph partitioning leads to more efficient workload allocation in SNN simulations, this thesis proposes a novel sequential hypergraph partitioning algorithm that incorporates network bandwidth via profiling. This leads to a significant reduction in execution time (up to 14x speedup in synthetic benchmark simulations compared to architecture-agnostic partitioners). The motivating context of this work is large scale brain simulations, however in the era of social media, large graphs and hypergraphs are increasingly relevant in many other scientific applications. A common feature of such graphs is that they are too big for a single machine to cope, both in terms of performance and memory requirements. State-of-the-art multilevel partitioning has been shown to struggle to scale to large graphs in distributed memory, not just because they take a long time to process, but also because they require full knowledge of the graph (not possible in dynamic graphs) and to fit the graph entirely in memory (not possible for very large graphs). To address those limitations we propose a parallel implementation of our architecture-aware streaming hypergraph partitioning algorithm (HyperPRAW) to model distributed applications. Results demonstrate that HyperPRAW produces consistent speedup over previous streaming approaches that only consider hyperedge overlap (up to 5.2x speedup). Compared to multilevel global partitioner in dense hypergraphs (those with high average cardinality), HyperPRAW is able to produce workload allocations that result in speeding up runtime in a synthetic simulation benchmark (up to 4.3x). HyperPRAW has the potential to scale to very large hypergraphs as it only requires local information to make allocation decisions, with an order of magnitude less memory footprint than global partitioners. The combined contributions of this thesis lead to a novel, parallel, scalable, streaming hypergraph partitioning algorithm (HyperPRAW) that can be used to help scale large distributed simulations in HPC systems. HyperPRAW helps tackle three of the main scalability challenges: it produces highly balanced distributed computation and communication, minimising idle time between computing nodes; it reduces the communication overhead by placing frequently communicating simulation elements close to each other (where the communication cost is minimal); and it provides a solution with a reasonable memory footprint that allows tackling larger problems than state-of-the-art alternatives such as global multilevel partitioning

Object detection and recognition with event driven cameras

This thesis presents study, analysis and implementation of algorithms to perform object detection and recognition using an event-based cam era. This sensor represents a novel paradigm which opens a wide range of possibilities for future developments of computer vision. In partic ular it allows to produce a fast, compressed, illumination invariant output, which can be exploited for robotic tasks, where fast dynamics and signi\ufb01cant illumination changes are frequent. The experiments are carried out on the neuromorphic version of the iCub humanoid platform. The robot is equipped with a novel dual camera setup mounted directly in the robot\u2019s eyes, used to generate data with a moving camera. The motion causes the presence of background clut ter in the event stream. In such scenario the detection problem has been addressed with an at tention mechanism, speci\ufb01cally designed to respond to the presence of objects, while discarding clutter. The proposed implementation takes advantage of the nature of the data to simplify the original proto object saliency model which inspired this work. Successively, the recognition task was \ufb01rst tackled with a feasibility study to demonstrate that the event stream carries su\ufb03cient informa tion to classify objects and then with the implementation of a spiking neural network. The feasibility study provides the proof-of-concept that events are informative enough in the context of object classi\ufb01 cation, whereas the spiking implementation improves the results by employing an architecture speci\ufb01cally designed to process event data. The spiking network was trained with a three-factor local learning rule which overcomes weight transport, update locking and non-locality problem. The presented results prove that both detection and classi\ufb01cation can be carried-out in the target application using the event data

Discovering Higher-order SNP Interactions in High-dimensional Genomic Data

In this thesis, a multifactor dimensionality reduction based method on associative classification is employed to identify higher-order SNP interactions for enhancing the understanding of the genetic architecture of complex diseases. Further, this thesis explored the application of deep learning techniques by providing new clues into the interaction analysis. The performance of the deep learning method is maximized by unifying deep neural networks with a random forest for achieving reliable interactions in the presence of noise

Simulated Annealing

The book contains 15 chapters presenting recent contributions of top researchers working with Simulated Annealing (SA). Although it represents a small sample of the research activity on SA, the book will certainly serve as a valuable tool for researchers interested in getting involved in this multidisciplinary field. In fact, one of the salient features is that the book is highly multidisciplinary in terms of application areas since it assembles experts from the fields of Biology, Telecommunications, Geology, Electronics and Medicine