Loihi is Intel's novel, manycore neuromorphic processor and is the first of its kind to feature a microcode-programmable learning engine that enables on-chip training of spiking neural networks (SNNs). The authors present the Loihi toolchain, which consists of an intuitive Python-based API for specifying SNNs, a compiler and runtime for building and executing SNNs on Loihi, and several target platforms (Loihi silicon, FPGA, and functional simulator). To showcase the toolchain, the authors describe how to build, train, and use a SNN to classify handwritten digits from the MNIST database.
This article presents a novel approach to monitoring athletes' behavioral changes to predict a decline in motivation. When the system detects such a decline, it refers the athlete to her coach, along with a concise explanation of the detected behavioral changes. The coach thus has all the information needed for a prompt, targeted intervention.
The adoption and effectiveness of cognitive assistive technologies hinge on harnessing the dynamics of human emotion. The authors discuss seminal advances in the integration of emotions in assistive technologies for dementia and propose Bayesian Affect Control Theory (BayesACT), a quantitative social-psychological theory, to model behavior and emotion in such systems.
Could combining quantum computing and machine learning with Moore's law produce a true "rebooted computer"? This article posits that a three-technology hybrid-computing approach might yield sufficiently improved answers to a broad class of problems such that energy efficiency will no longer be the dominant concern.
Tailoring cloud support for each autonomous-driving application would require maintaining multiple infrastructures, potentially resulting in low resource utilization, low performance, and high management overhead. To address this problem, the authors present a unified cloud infrastructure with Spark for distributed computing, Alluxio for distributed storage, and OpenCL to exploit heterogeneous computing resources for enhanced performance and energy efficiency.
Europe continues to be among the leaders in developing ground vehicles capable of real-time vision. At Bundeswehr University Munich (UniBwM), researchers are investigating "scout-type" vision for autonomous cars, which--unlike popular systems in use now--does not rely on accurate maps, GPS positioning, or databases of previously observed objects.
Many recent technological advances have helped to pave the way forward for fully autonomous vehicles. This special issue explores three aspects of the self-driving car revolution: a historical perspective with a focus on perception for autonomous vehicles, how government policy will impact self-driving cars technically and commercially, and how cloud-based infrastructure plays a role in the future.