54 research outputs found
NeuroFlow: A General Purpose Spiking Neural Network Simulation Platform using Customizable Processors
© 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
QUASII: QUery-Aware Spatial Incremental Index.
With large-scale simulations of increasingly detailed models and improvement of data acquisition technologies, massive amounts of data are easily and quickly created and collected. Traditional systems require indexes to be built before analytic queries can be executed efficiently. Such an indexing step requires substantial computing resources and introduces a considerable and growing data-to-insight gap where scientists need to wait before they can perform any analysis. Moreover, scientists often only use a small fraction of the data - the parts containing interesting phenomena - and indexing it fully does not always pay off. In this paper we develop a novel incremental index for the exploration of spatial data. Our approach, QUASII, builds a data-oriented index as a side-effect of query execution. QUASII distributes the cost of indexing across all queries, while building the index structure only for the subset of data queried. It reduces data-to-insight time and curbs the cost of incremental indexing by gradually and partially sorting the data, while producing a data-oriented hierarchical structure at the same time. As our experiments show, QUASII reduces the data-to-insight time by up to a factor of 11.4x, while its performance converges to that of the state-of-the-art static indexes
Towards Simulating the Human Brain
The human brain has been described as “the most complex object in the universe.” Its meshwork of 86 billion neurons,84 billion glial cells, and over 150 trillion synapses may seem intractable. Nonetheless, efforts to comprehensively map, understand, and even computationally reproduce this structure are underway. Large collectives of researchers have come together, working in concert towards these goals. The Human Brain Project (HBP) and its precursor, the Blue Brain Project, have spearheaded the brain simulation goal.Some other notable organizations include the China Brain Project, the BRAIN Initiative. On a scale which parallels the space program and the Human Genome Project, neuroscience may be approaching a revolution
Full Wafer Redistribution and Wafer Embedding as Key Technologies for a Multi-Scale Neuromorphic Hardware Cluster
Together with the Kirchhoff-Institute for Physics(KIP) the Fraunhofer IZM has
developed a full wafer redistribution and embedding technology as base for a
large-scale neuromorphic hardware system. The paper will give an overview of
the neuromorphic computing platform at the KIP and the associated hardware
requirements which drove the described technological developments. In the first
phase of the project standard redistribution technologies from wafer level
packaging were adapted to enable a high density reticle-to-reticle routing on
200mm CMOS wafers. Neighboring reticles were interconnected across the scribe
lines with an 8{\mu}m pitch routing based on semi-additive copper
metallization. Passivation by photo sensitive benzocyclobutene was used to
enable a second intra-reticle routing layer. Final IO pads with flash gold were
generated on top of each reticle. With that concept neuromorphic systems based
on full wafers could be assembled and tested. The fabricated high density
inter-reticle routing revealed a very high yield of larger than 99.9%. In order
to allow an upscaling of the system size to a large number of wafers with
feasible effort a full wafer embedding concept for printed circuit boards was
developed and proven in the second phase of the project. The wafers were
thinned to 250{\mu}m and laminated with additional prepreg layers and copper
foils into a core material. After lamination of the PCB panel the reticle IOs
of the embedded wafer were accessed by micro via drilling, copper
electroplating, lithography and subtractive etching of the PCB wiring
structure. The created wiring with 50um line width enabled an access of the
reticle IOs on the embedded wafer as well as a board level routing. The panels
with the embedded wafers were subsequently stressed with up to 1000 thermal
cycles between 0C and 100C and have shown no severe failure formation over the
cycle time.Comment: Accepted at EPTC 201
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