91 research outputs found
BĀ²NĀ²: Resource efficient Bayesian neural network accelerator using Bernoulli sampler on FPGA
A resource efficient hardware accelerator for Bayesian neural network (BNN) named BĀ²NĀ², Bernoulli random number based Bayesian neural network accelerator, is proposed. As neural networks expand their application into risk sensitive domains where mispredictions may cause serious social and economic losses, evaluating the NNās confidence on its prediction has emerged as a critical concern. Among many uncertainty evaluation methods, BNN provides a theoretically grounded way to evaluate the uncertainty of NNās output by treating network parameters as random variables. By exploiting the central limit theorem, we propose to replace costly Gaussian random number generators (RNG) with Bernoulli RNG which can be efficiently implemented on hardware since the possible outcome from Bernoulli distribution is binary. We demonstrate that BĀ²NĀ² implemented on Xilinx ZCU104 FPGA board consumes only 465 DSPs and 81661 LUTs which corresponds to 50.9% and 14.3% reductions compared to Gaussian-BNN (Hirayama et al., 2020) implemented on the same FPGA board for fair comparison. We further compare BĀ²NĀ² with VIBNN (Cai et al., 2018), which shows that BĀ²NĀ² successfully reduced DSPs and LUTs usages by 50.9% and 57.9%, respectively. Owing to the reduced hardware resources, BĀ²NĀ² improved energy efficiency by 7.50% and 57.5% compared to Gaussian-BNN (Hirayama et al., 2020) and VIBNN (Cai et al., 2018), respectively
Design Exploration of an FPGA-Based Multivariate Gaussian Random Number Generator
Monte Carlo simulation is one of the most widely used techniques for computationally
intensive simulations in a variety of applications including mathematical
analysis and modeling and statistical physics. A multivariate Gaussian
random number generator (MVGRNG) is one of the main building blocks of
such a system. Field Programmable Gate Arrays (FPGAs) are gaining increased
popularity as an alternative means to the traditional general purpose
processors targeting the acceleration of the computationally expensive random
number generator block due to their fine grain parallelism and reconfigurability
properties and lower power consumption.
As well as the ability to achieve hardware designs with high throughput it
is also desirable to produce designs with the flexibility to control the resource
usage in order to meet given resource constraints. This work proposes a novel
approach for mapping a MVGRNG onto an FPGA by optimizing the computational
path in terms of hardware resource usage subject to an acceptable
error in the approximation of the distribution of interest. An analysis on the
impact of the error due to truncation/rounding operation along the computational path is performed and an analytical expression of the error inserted into
the system is presented.
Extra dimensionality is added to the feature of the proposed algorithm by
introducing a novel methodology to map many multivariate Gaussian random
number generators onto a single FPGA. The effective resource sharing techniques
introduced in this thesis allows further reduction in hardware resource
usage.
The use of MVGNRG can be found in a wide range of application, especially
in financial applications which involve many correlated assets. In this
work it is demonstrated that the choice of the objective function employed
for the hardware optimization of the MVRNG core has a considerable impact
on the final performance of the application of interest. Two of the most important
financial applications, Value-at-Risk estimation and option pricing are
considered in this work
Continuous-variable multipartite unlockable bound entangled Gaussian states
Continuous-variable (CV) multipartite unlockable bound-entangled states is
investigated in this paper. Comparing with the qubit multipartite unlockable
bound-entangled states, CV multipartite unlockable bound-entangled states
present the new and different properties. CV multipartite unlockable
bound-entangled states may serve as a useful quantum resource for new
multiparty communication schemes. The experimental protocol for generating CV
unlockable bound-entangled states is proposed with a setup that is at present
accessible.Comment: 6 pages, 4 figure
A More Reliable Greedy Heuristic for Maximum Matchings in Sparse Random Graphs
We propose a new greedy algorithm for the maximum cardinality matching
problem. We give experimental evidence that this algorithm is likely to find a
maximum matching in random graphs with constant expected degree c>0,
independent of the value of c. This is contrary to the behavior of commonly
used greedy matching heuristics which are known to have some range of c where
they probably fail to compute a maximum matching
On the Efficiency of Simplified Weak Taylor Schemes for Monte Carlo Simulation in Finance
The purpose of this paper is to study the efficiency of simplified weak schemes for stochastic differential equations. We present a numerical comparison between weak Taylor schemes and their simplified versions. In the simplified schemes discrete random variables, instead of Gaussian ones, are generated to approximate multiple stochastic integrals. We show that an implementation of simplified schemes based on random bits generators significantly increases the computational speed. The efficiency of the proposed schemes is demonstrated.random bits generators; stochastic differential equations; simplified weak taylor schemes
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