Current and Nascent SETI Instruments

Here we describe our ongoing efforts to develop high-performance and sensitive instrumentation for use in the search for extra-terrestrial intelligence (SETI). These efforts include our recently deployed Search for Extraterrestrial Emissions from Nearby Developed Intelligent Populations Spectrometer (SERENDIP V.v) and two instruments currently under development; the Heterogeneous Radio SETI Spectrometer (HRSS) for SETI observations in the radio spectrum and the Optical SETI Fast Photometer (OSFP) for SETI observations in the optical band. We will discuss the basic SERENDIP V.v instrument design and initial analysis methodology, along with instrument architectures and observation strategies for OSFP and HRSS. In addition, we will demonstrate how these instruments may be built using low-cost, modular components and programmed and operated by students using common languages, e.g. ANSI C.Comment: 12 pages, 5 figures, Original version appears as Chapter 2 in "The Proceedings of SETI Sessions at the 2010 Astrobiology Science Conference: Communication with Extraterrestrial Intelligence (CETI)," Douglas A. Vakoch, Edito

Debugging mixed-signal circuits via the IEEE1149.4 Std. - analysis of limitations and requirements

Debugging mixed-signal circuits is traditionally seen as a complex task due to the presence of an analog part and the necessary interaction with a digital part. The use of debug tools that require physical access suffers from the same restrictions that led to the use of debug tools based on electronic access to digital circuits. While the IEEE1149.4 test infrastructure enables the structural and parametric test of mixed-signal boards, through electronic access, its use for debug purposes is still far from reaching a wide acceptance, namely due to the lack of a debug methodology. This work analyses several access mechanisms for Controllability, Observability and Verification operations via the IEEE1149.4 infrastructure, with an emphasis on the analysis of its limitations and requirements

Versatile stochastic dot product circuits based on nonvolatile memories for high performance neurocomputing and neurooptimization.

The key operation in stochastic neural networks, which have become the state-of-the-art approach for solving problems in machine learning, information theory, and statistics, is a stochastic dot-product. While there have been many demonstrations of dot-product circuits and, separately, of stochastic neurons, the efficient hardware implementation combining both functionalities is still missing. Here we report compact, fast, energy-efficient, and scalable stochastic dot-product circuits based on either passively integrated metal-oxide memristors or embedded floating-gate memories. The circuit's high performance is due to mixed-signal implementation, while the efficient stochastic operation is achieved by utilizing circuit's noise, intrinsic and/or extrinsic to the memory cell array. The dynamic scaling of weights, enabled by analog memory devices, allows for efficient realization of different annealing approaches to improve functionality. The proposed approach is experimentally verified for two representative applications, namely by implementing neural network for solving a four-node graph-partitioning problem, and a Boltzmann machine with 10-input and 8-hidden neurons

A Comprehensive Workflow for General-Purpose Neural Modeling with Highly Configurable Neuromorphic Hardware Systems

In this paper we present a methodological framework that meets novel requirements emerging from upcoming types of accelerated and highly configurable neuromorphic hardware systems. We describe in detail a device with 45 million programmable and dynamic synapses that is currently under development, and we sketch the conceptual challenges that arise from taking this platform into operation. More specifically, we aim at the establishment of this neuromorphic system as a flexible and neuroscientifically valuable modeling tool that can be used by non-hardware-experts. We consider various functional aspects to be crucial for this purpose, and we introduce a consistent workflow with detailed descriptions of all involved modules that implement the suggested steps: The integration of the hardware interface into the simulator-independent model description language PyNN; a fully automated translation between the PyNN domain and appropriate hardware configurations; an executable specification of the future neuromorphic system that can be seamlessly integrated into this biology-to-hardware mapping process as a test bench for all software layers and possible hardware design modifications; an evaluation scheme that deploys models from a dedicated benchmark library, compares the results generated by virtual or prototype hardware devices with reference software simulations and analyzes the differences. The integration of these components into one hardware-software workflow provides an ecosystem for ongoing preparative studies that support the hardware design process and represents the basis for the maturity of the model-to-hardware mapping software. The functionality and flexibility of the latter is proven with a variety of experimental results

The MEG detector for μ+→e+γ{\mu}+\to e+{\gamma} decay search

The MEG (Mu to Electron Gamma) experiment has been running at the Paul Scherrer Institut (PSI), Switzerland since 2008 to search for the decay \meg\ by using one of the most intense continuous $\mu^+$ beams in the world. This paper presents the MEG components: the positron spectrometer, including a thin target, a superconducting magnet, a set of drift chambers for measuring the muon decay vertex and the positron momentum, a timing counter for measuring the positron time, and a liquid xenon detector for measuring the photon energy, position and time. The trigger system, the read-out electronics and the data acquisition system are also presented in detail. The paper is completed with a description of the equipment and techniques developed for the calibration in time and energy and the simulation of the whole apparatus.Comment: 59 pages, 90 figure