Neural network based architectures for aerospace applications

A brief history of the field of neural networks research is given and some simple concepts are described. In addition, some neural network based avionics research and development programs are reviewed. The need for the United States Air Force and NASA to assume a leadership role in supporting this technology is stressed

The intelligence of sheep

This commentary suggests how recent theories about the predictive brain could help us understand the evidence put forward by Marino & Merskin for intelligence in sheep. I contrast predictive intelligence in sheep with automatic behaviors that do not require intelligence, and I consider the flexibility of sheep intelligence

The intelligence of sheep

This commentary suggests how recent theories about the predictive brain could help us understand the evidence put forward by Marino & Merskin for intelligence in sheep. I contrast predictive intelligence in sheep with automatic behaviors that do not require intelligence, and I consider the flexibility of sheep intelligence

Dresdner Universitätsjournal

Dresdner Universitätsjournal vom 19. April 201

Small business innovation research. Abstracts of 1988 phase 1 awards

Non-proprietary proposal abstracts of Phase 1 Small Business Innovation Research (SBIR) projects supported by NASA are presented. Projects in the fields of aeronautical propulsion, aerodynamics, acoustics, aircraft systems, materials and structures, teleoperators and robots, computer sciences, information systems, data processing, spacecraft propulsion, bioastronautics, satellite communication, and space processing are covered

In-Datacenter Performance Analysis of a Tensor Processing Unit

Many architects believe that major improvements in cost-energy-performance must now come from domain-specific hardware. This paper evaluates a custom ASIC---called a Tensor Processing Unit (TPU)---deployed in datacenters since 2015 that accelerates the inference phase of neural networks (NN). The heart of the TPU is a 65,536 8-bit MAC matrix multiply unit that offers a peak throughput of 92 TeraOps/second (TOPS) and a large (28 MiB) software-managed on-chip memory. The TPU's deterministic execution model is a better match to the 99th-percentile response-time requirement of our NN applications than are the time-varying optimizations of CPUs and GPUs (caches, out-of-order execution, multithreading, multiprocessing, prefetching, ...) that help average throughput more than guaranteed latency. The lack of such features helps explain why, despite having myriad MACs and a big memory, the TPU is relatively small and low power. We compare the TPU to a server-class Intel Haswell CPU and an Nvidia K80 GPU, which are contemporaries deployed in the same datacenters. Our workload, written in the high-level TensorFlow framework, uses production NN applications (MLPs, CNNs, and LSTMs) that represent 95% of our datacenters' NN inference demand. Despite low utilization for some applications, the TPU is on average about 15X - 30X faster than its contemporary GPU or CPU, with TOPS/Watt about 30X - 80X higher. Moreover, using the GPU's GDDR5 memory in the TPU would triple achieved TOPS and raise TOPS/Watt to nearly 70X the GPU and 200X the CPU.Comment: 17 pages, 11 figures, 8 tables. To appear at the 44th International Symposium on Computer Architecture (ISCA), Toronto, Canada, June 24-28, 201

Digitalization Innovations: Neurotechnologies and Robots in Inclusive Education Process

В статье рассматриваются инновационные тренды цифровизации современного инклюзивного образования.Modern general, special and inclusive education is a zone of multiple innovations, including digital ones. Under these conditions, it is extremely important to formulate and solve the problem associated with localizing the place of digital, including neurodigital and robot technologies, in inclusive education, and identifying and correcting notorious and quasi-profes¬sional myths and mistakes of “digitalization” and the related transformations of education processes and environments. The purpose of the study is to analyze innovative trends of digitalization of the modern inclusive education: the opportunities and limitations of modern neurotechnologies and robots in the inclusive educational dialogue. The re¬search method is based on theoretical analysis and synthesis of innovative trends of digitalization of the modern inclusive education