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

Deep learning-based anomalous object detection system powered by microcontroller for PTZ cameras

Automatic video surveillance systems are usually designed to detect anomalous objects being present in a scene or behaving dangerously. In order to perform adequately, they must incorporate models able to achieve accurate pattern recognition in an image, and deep learning neural networks excel at this task. However, exhaustive scan of the full image results in multiple image blocks or windows to analyze, which could make the time performance of the system very poor when implemented on low cost devices. This paper presents a system which attempts to detect abnormal moving objects within an area covered by a PTZ camera while it is panning. The decision about the block of the image to analyze is based on a mixture distribution composed of two components: a uniform probability distribution, which represents a blind random selection, and a mixture of Gaussian probability distributions. Gaussian distributions represent windows in the image where anomalous objects were detected previously and contribute to generate the next window to analyze close to those windows of interest. The system is implemented on a Raspberry Pi microcontroller-based board, which enables the design and implementation of a low-cost monitoring system that is able to perform image processing.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Parallel proccessing applied to object detection with a Jetson TX2 embedded system.

Video streams from panoramic cameras represent a powerful tool for automated surveillance systems, but naïve implementations typically require very intensive computational loads for applying deep learning models for automated detection and tracking of objects of interest, since these models require relatively high resolution to reliably perform object detection. In this paper, we report a host of improvements to our previous state-of-the-art software system to reliably detect and track objects in video streams from panoramic cameras, resulting in an increase in the processing framerate in a Jetson TX2 board, with respect to our previous results. Depending on the number of processes and the load profile, we observe up to a five-fold increase in the framerate.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech