The modelling of large systems of spiking neurons is computationally very demanding in terms of processing power and communication. SpiNNaker-Spiking Neural Network architecture-is a massively parallel computer system designed to provide a cost-effective and flexible simulator for neuroscience experiments. It can model up to a billion neurons and a trillion synapses in biological real time. The basic building block is the SpiNNaker Chip Multiprocessor (CMP), which is a custom-designed globally asynchronous locally synchronous (GALS) system with 18 ARM968 processor nodes residing in synchronous islands, surrounded by a lightweight, packet-switched asynchronous communications infrastructure. In this paper, we review the design requirements for its very demanding target application, the SpiNNaker micro-architecture and its implementation issues. We also evaluate the SpiNNaker CMP, which contains 100 million transistors in a 102-mm die, provides a peak performance of 3.96 GIPS, and has a peak power consumption of 1 W when all processor cores operate at the nominal frequency of 180 MHz. SpiNNaker chips are fully operational and meet their power and performance requirements.Index Terms-Asynchronous interconnect, chip multiprocessor, energy efficiency, globally asynchronous locally synchronous (GALS), network-on-chip, neuromorphic hardware, real-time simulation, spiking neural networks (SNNs).

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The eight-meter-wavelength transient array (ETA) is a new radio telescope consisting of 12 dual-polarized, 38 MHz-resonant dipole elements which are individually instrumented, digitized, and analyzed in an attempt to detect rare and as-yet undetected single dispersed pulses believed to be associated with certain types of astronomical explosions. This paper presents the design and demonstrated performance of ETA's dipole antennas. An inverted V-shaped design combined with a simple and inexpensive active balun yields sensitivity which is limited only by the external noise generated by the ubiquitous Galactic synchrotron emission over a range greater than the 27-49 MHz design range. The results confirm findings from a recent theoretical analysis, and the techniques described here may have applications in other problems requiring in situ evaluation of large low-frequency antennas.

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A large research team with a wide range of expertiseVfrom ICs and reconfigurable computing to wireless networkingVworks to achieve the promise of cognitive radio. ABSTRACT | More than a dozen Wireless @ Virginia Tech faculty are working to address the broad research agenda of cognitive radio and cognitive networks. Our core research team spans the protocol stack from radio and reconfigurable hardware to communications theory to the networking layer.Our work includes new analysis methods and the development of new software architectures and applications, in addition to work on the core concepts and architectures underlying cognitive radios and cognitive networks. This paper describes these contributions and points towards critical future work that remains to fulfill the promise of cognitive radio. We briefly describe the history of work on cognitive radios and networks at Virginia Tech and then discuss our contributions to the core cognitive processing underlying these systems, focusing on our cognitive engine. We also describe developments that support the cognitive engine and advances in radio technology that provide the flexibility desired in a cognitive radio node. We consider securing and verifying cognitive systems and examine the challenges of expanding the cognitive paradigm up the protocol stack to optimize end-to-end network performance.Lastly, we consider the analysis of cognitive systems using game theory and the application of cognitive techniques to problems in dynamic spectrum sharing and control of multipleinput multiple-output radios.

Two cases of extraadrenal myelolipoma with unusual clinicopathologic features are described. The patient in case 1 had multifocal myelolipomas, involving both the lung and retroperitoneum, that mimicked malignancy, and the patient in case 2 had a single diffuse retroperitoneal myelolipoma without circumscription. Of possible etiologic significance is the fact that both patients were treated with exogenous steroids.

Abstract-The modelling of large systems of spiking neurons is computationally very demanding in terms of processing power and communication. SpiNNaker is a massively-parallel computer system designed to model up to a billion spiking neurons in real time. The basic block of the machine is the SpiNNaker multicore System-on-Chip, a Globally Asynchronous Locally Synchronous (GALS) system with 18 ARM968 processor nodes residing in synchronous islands, surrounded by a light-weight, packet-switched asynchronous communications infrastructure. The MPSoC contains 100 million transistors in a 102 mm 2 die, provides a peak performance of 3.96 GIPS and has a power consumption of 1W at 1.2V when all processor cores operate at nominal frequency. SpiNNaker chips were delivered in May 2011, were fully operational, and met power and performance requirements.