Is neuromorphic computing disruptive enough to 1) advance our understanding of the brain and 2) make the design and working of (bio)electronic devices efficient and scalable?

Neuromorphic computing is an approach to computing that is inspired by the structure and function of the brain. In terms of basic research, it aims at improving our understanding of biological intelligence by replicating aspects of its physical substrate – spiking neurons, synaptic plasticity etc. – and harness them towards also replicating its function. With respect to technological advances, it aims to inherit the brain’s combination of computational prowess and extreme energy efficiency; this is thought to foster a plethora of applications, from large-scale neuromorphic systems for machine learning to small-scale edge devices for signal processing and control, for example in the form of wearables for healthcare or adaptive sensors/processors for autonomous agents. The demand and usefulness of neuromorphic computing in bioelectronics is likely to increase in the future as researchers continue to explore its capabilities and develop new applications

Hybrid mobile computing for connected autonomous vehicles

With increasing urbanization and the number of cars on road, there are many global issues on modern transport systems, Autonomous driving and connected vehicles are the most promising technologies to tackle these issues. The so-called integrated technology connected autonomous vehicles (CAV) can provide a wide range of safety applications for safer, greener and more efficient intelligent transport systems (ITS). As computing is an extreme component for CAV systems,various mobile computing models including mobile local computing, mobile edge computing and mobile cloud computing are proposed. However it is believed that none of these models fits all CAV applications, which have highly diverse quality of service (QoS) requirements such as communication delay, data rate, accuracy, reliability and/or computing latency.In this thesis, we are motivated to propose a hybrid mobile computing model with objective of overcoming limitations of individual models and maximizing the performances for CAV applications.In proposed hybrid mobile computing model three basic computing models and/or their combinations are chosen and applied to different CAV applications, which include mobile local computing, mobile edge computing and mobile cloud computing. Different computing models and their combinations are selected according to the QoS requirements of the CAV applications.Following the idea, we first investigate the job offloading and allocation of computing and communication resources at the local hosts and external computing centers with QoS aware and resource awareness. Distributed admission control and resource allocation algorithms are proposed including two baseline non-cooperative algorithms and a matching theory based cooperative algorithm. Experiment results demonstrate the feasibility of the hybrid mobile computing model and show large improvement on the service quality and capacity over existing individual computing models. The matching algorithm also largely outperforms the baseline non-cooperative algorithms.In addition, two specific use cases of the hybrid mobile computing for CAV applications are investigated: object detection with mobile local computing where only local computing resources are used, and movie recommendation with mobile cloud computing where remote cloud resources are used. For object detection, we focus on the challenges of detecting vehicles, pedestrians and cyclists in driving environment and propose three methods to an existing CNN based object detector. Large detection performance improvement is obtained over the KITTI benchmark test dataset. For movie recommendation we propose two recommendation models based on a general framework of integrating machine learning and collaborative filtering approach.The experiment results on Netix movie dataset show that our models are very effective for cold start items recommendatio

Computing symmetry groups of polyhedra

Knowing the symmetries of a polyhedron can be very useful for the analysis of its structure as well as for practical polyhedral computations. In this note, we study symmetry groups preserving the linear, projective and combinatorial structure of a polyhedron. In each case we give algorithmic methods to compute the corresponding group and discuss some practical experiences. For practical purposes the linear symmetry group is the most important, as its computation can be directly translated into a graph automorphism problem. We indicate how to compute integral subgroups of the linear symmetry group that are used for instance in integer linear programming.Comment: 20 pages, 1 figure; containing a corrected and improved revisio

Similarity-Aware Spectral Sparsification by Edge Filtering

In recent years, spectral graph sparsification techniques that can compute ultra-sparse graph proxies have been extensively studied for accelerating various numerical and graph-related applications. Prior nearly-linear-time spectral sparsification methods first extract low-stretch spanning tree from the original graph to form the backbone of the sparsifier, and then recover small portions of spectrally-critical off-tree edges to the spanning tree to significantly improve the approximation quality. However, it is not clear how many off-tree edges should be recovered for achieving a desired spectral similarity level within the sparsifier. Motivated by recent graph signal processing techniques, this paper proposes a similarity-aware spectral graph sparsification framework that leverages efficient spectral off-tree edge embedding and filtering schemes to construct spectral sparsifiers with guaranteed spectral similarity (relative condition number) level. An iterative graph densification scheme is introduced to facilitate efficient and effective filtering of off-tree edges for highly ill-conditioned problems. The proposed method has been validated using various kinds of graphs obtained from public domain sparse matrix collections relevant to VLSI CAD, finite element analysis, as well as social and data networks frequently studied in many machine learning and data mining applications