3,646 research outputs found

    Using MCD-DVS for dynamic thermal management performance improvement

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    With chip temperature being a major hurdle in microprocessor design, techniques to recover the performance loss due to thermal emergency mechanisms are crucial in order to sustain performance growth. Many techniques for power reduction in the past and some on thermal management more recently have contributed to alleviate this problem. Probably the most important thermal control technique is dynamic voltage and frequency scaling (DVS) which allows for almost cubic reduction in power with worst-case performance penalty only linear. So far, DVS techniques for temperature control have been studied at the chip level. Finer grain DVS is feasible if a globally-asynchronous locally-synchronous (GALS) design style is employed. GALS, also known as multiple-clock domain (MCD), allows for an independent voltage and frequency control for each one of the clock domains that are part of the chip. There are several studies on DVS for GALS that aim to improve energy and power efficiency but not temperature. This paper proposes and analyses the usage of DVS at the domain level to control temperature in a clustered MCD microarchitecture with the goal of improving the performance of applications that do not meet the thermal constraints imposed by the designers.Peer ReviewedPostprint (published version

    Bioinspired Computing: Swarm Intelligence

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    Hormone deprivation alters mitochondrial function and lipid profile in the hippocampus

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    Mitochondrial dysfunction is a common hallmark in aging. In the female, reproductive senescence is characterized by loss of ovarian hormones, many of whose neuroprotective effects converge upon mitochondria. The functional integrity of mitochondria is dependent on membrane fatty acid and phospholipid composition, which are also affected during aging. The effect of long-term ovarian hormone deprivation upon mitochondrial function and its putative association with changes in mitochondrial membrane lipid profile in the hippocampus, an area primarily affected during aging and highly responsive to ovarian hormones, is unknown. To this aim, Wistar adult female rats were ovariectomized or sham-operated. Twelve weeks later, different parameters of mitochondrial function (O2 uptake, ATP production, membrane potential and respiratory complex activities) as well as membrane phospholipid content and composition were evaluated in hippocampal mitochondria. Chronic ovariectomy reduced mitochondrial O2 uptake and ATP production rates and induced membrane depolarization during active respiration without altering the activity of respiratory complexes. Mitochondrial membrane lipid profile showed no changes in cholesterol levels but higher levels of unsaturated fatty acids and a higher peroxidizability index in mitochondria from ovariectomized rats. Interestingly, ovariectomy also reduced cardiolipin content and altered cardiolipin fatty acid profile leading to a lower peroxidizability index. In conclusion, chronic ovarian hormone deprivation induces mitochondrial dysfunction and changes in the mitochondrial membrane lipid profile comparable to an aging phenotype. Our study provides insights into ovarian hormone loss-induced early lipidomic changes with bioenergetic deficits in the hippocampus that may contribute to the increased risk of Alzheimer’s disease and other age-associated disorders observed in postmenopause.Fil: Zarate, Sandra Cristina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Biomédicas. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Biomédicas; ArgentinaFil: Astiz, Mariana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Bioquímicas de La Plata "Prof. Dr. Rodolfo R. Brenner". Universidad Nacional de la Plata. Facultad de Ciencias Médicas. Instituto de Investigaciones Bioquímicas de La Plata ; ArgentinaFil: Magnani, Natalia Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Bioquímica y Medicina Molecular. Universidad de Buenos Aires. Facultad Medicina. Instituto de Bioquímica y Medicina Molecular; ArgentinaFil: Imsen, Mercedes. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Biomédicas. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Biomédicas; ArgentinaFil: Merino, Florencia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Biomédicas. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Biomédicas; ArgentinaFil: Alvarez, Silvia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Bioquímica y Medicina Molecular. Universidad de Buenos Aires. Facultad Medicina. Instituto de Bioquímica y Medicina Molecular; ArgentinaFil: Reines, Analia Gabriela. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Biología Celular y Neurociencia "Prof. Eduardo de Robertis". Universidad de Buenos Aires. Facultad de Medicina. Instituto de Biología Celular y Neurociencia; ArgentinaFil: Seilicovich, Adriana. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Biomédicas. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Biomédicas; Argentin

    NeuroFlow: A General Purpose Spiking Neural Network Simulation Platform using Customizable Processors

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    © 2016 Cheung, Schultz and Luk.NeuroFlow is a scalable spiking neural network simulation platform for off-the-shelf high performance computing systems using customizable hardware processors such as Field-Programmable Gate Arrays (FPGAs). Unlike multi-core processors and application-specific integrated circuits, the processor architecture of NeuroFlow can be redesigned and reconfigured to suit a particular simulation to deliver optimized performance, such as the degree of parallelism to employ. The compilation process supports using PyNN, a simulator-independent neural network description language, to configure the processor. NeuroFlow supports a number of commonly used current or conductance based neuronal models such as integrate-and-fire and Izhikevich models, and the spike-timing-dependent plasticity (STDP) rule for learning. A 6-FPGA system can simulate a network of up to ~600,000 neurons and can achieve a real-time performance of 400,000 neurons. Using one FPGA, NeuroFlow delivers a speedup of up to 33.6 times the speed of an 8-core processor, or 2.83 times the speed of GPU-based platforms. With high flexibility and throughput, NeuroFlow provides a viable environment for large-scale neural network simulation
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