1,025 research outputs found
Customisable arithmetic hardware designs
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Accelerating Reconfigurable Financial Computing
This thesis proposes novel approaches to the design, optimisation, and management of reconfigurable
computer accelerators for financial computing. There are three contributions. First, we propose novel
reconfigurable designs for derivative pricing using both Monte-Carlo and quadrature methods. Such
designs involve exploring techniques such as control variate optimisation for Monte-Carlo, and multi-dimensional
analysis for quadrature methods. Significant speedups and energy savings are achieved
using our Field-Programmable Gate Array (FPGA) designs over both Central Processing Unit (CPU)
and Graphical Processing Unit (GPU) designs. Second, we propose a framework for distributing computing
tasks on multi-accelerator heterogeneous clusters. In this framework, different computational
devices including FPGAs, GPUs and CPUs work collaboratively on the same financial problem based
on a dynamic scheduling policy. The trade-off in speed and in energy consumption of different accelerator
allocations is investigated. Third, we propose a mixed precision methodology for optimising
Monte-Carlo designs, and a reduced precision methodology for optimising quadrature designs. These
methodologies enable us to optimise throughput of reconfigurable designs by using datapaths with
minimised precision, while maintaining the same accuracy of the results as in the original designs
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TAO Conceptual Design Report: A Precision Measurement of the Reactor Antineutrino Spectrum with Sub-percent Energy Resolution
The Taishan Antineutrino Observatory (TAO, also known as JUNO-TAO) is a
satellite experiment of the Jiangmen Underground Neutrino Observatory (JUNO). A
ton-level liquid scintillator detector will be placed at about 30 m from a core
of the Taishan Nuclear Power Plant. The reactor antineutrino spectrum will be
measured with sub-percent energy resolution, to provide a reference spectrum
for future reactor neutrino experiments, and to provide a benchmark measurement
to test nuclear databases. A spherical acrylic vessel containing 2.8 ton
gadolinium-doped liquid scintillator will be viewed by 10 m^2 Silicon
Photomultipliers (SiPMs) of >50% photon detection efficiency with almost full
coverage. The photoelectron yield is about 4500 per MeV, an order higher than
any existing large-scale liquid scintillator detectors. The detector operates
at -50 degree C to lower the dark noise of SiPMs to an acceptable level. The
detector will measure about 2000 reactor antineutrinos per day, and is designed
to be well shielded from cosmogenic backgrounds and ambient radioactivities to
have about 10% background-to-signal ratio. The experiment is expected to start
operation in 2022
Source-device-independent heterodyne-based quantum random number generator at 17 Gbps
For many applications, quantum random number generation should be fast and independent from assumptions on the apparatus. Here, the authors devise and implement an approach which assumes a trusted detector but not a trusted source, and allows random bit generations at ~17 Gbps using off-the-shelf components
Demonstrating Advantages of Neuromorphic Computation: A Pilot Study
Neuromorphic devices represent an attempt to mimic aspects of the brain's
architecture and dynamics with the aim of replicating its hallmark functional
capabilities in terms of computational power, robust learning and energy
efficiency. We employ a single-chip prototype of the BrainScaleS 2 neuromorphic
system to implement a proof-of-concept demonstration of reward-modulated
spike-timing-dependent plasticity in a spiking network that learns to play the
Pong video game by smooth pursuit. This system combines an electronic
mixed-signal substrate for emulating neuron and synapse dynamics with an
embedded digital processor for on-chip learning, which in this work also serves
to simulate the virtual environment and learning agent. The analog emulation of
neuronal membrane dynamics enables a 1000-fold acceleration with respect to
biological real-time, with the entire chip operating on a power budget of 57mW.
Compared to an equivalent simulation using state-of-the-art software, the
on-chip emulation is at least one order of magnitude faster and three orders of
magnitude more energy-efficient. We demonstrate how on-chip learning can
mitigate the effects of fixed-pattern noise, which is unavoidable in analog
substrates, while making use of temporal variability for action exploration.
Learning compensates imperfections of the physical substrate, as manifested in
neuronal parameter variability, by adapting synaptic weights to match
respective excitability of individual neurons.Comment: Added measurements with noise in NEST simulation, add notice about
journal publication. Frontiers in Neuromorphic Engineering (2019
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