MASSIVELY PARALLEL ALGORITHMS FOR POINT CLOUD BASED OBJECT RECOGNITION ON HETEROGENEOUS ARCHITECTURE

With the advent of new commodity depth sensors, point cloud data processing plays an increasingly important role in object recognition and perception. However, the computational cost of point cloud data processing is extremely high due to the large data size, high dimensionality, and algorithmic complexity. To address the computational challenges of real-time processing, this work investigates the possibilities of using modern heterogeneous computing platforms and its supporting ecosystem such as massively parallel architecture (MPA), computing cluster, compute unified device architecture (CUDA), and multithreaded programming to accelerate the point cloud based object recognition. The aforementioned computing platforms would not yield high performance unless the specific features are properly utilized. Failing that the result actually produces an inferior performance. To achieve the high-speed performance in image descriptor computing, indexing, and matching in point cloud based object recognition, this work explores both coarse and fine grain level parallelism, identifies the acceptable levels of algorithmic approximation, and analyzes various performance impactors. A set of heterogeneous parallel algorithms are designed and implemented in this work. These algorithms include exact and approximate scalable massively parallel image descriptors for descriptor computing, parallel construction of k-dimensional tree (KD-tree) and the forest of KD-trees for descriptor indexing, parallel approximate nearest neighbor search (ANNS) and buffered ANNS (BANNS) on the KD-tree and the forest of KD-trees for descriptor matching. The results show that the proposed massively parallel algorithms on heterogeneous computing platforms can significantly improve the execution time performance of feature computing, indexing, and matching. Meanwhile, this work demonstrates that the heterogeneous computing architectures, with appropriate architecture specific algorithms design and optimization, have the distinct advantages of improving the performance of multimedia applications

Towards a real-time 3D object recognition pipeline on mobile GPGPU computing platforms using low-cost RGB-D sensors

In this project, we propose the implementation of a 3D object recognition system which will be optimized to operate under demanding time constraints. The system must be robust so that objects can be recognized properly in poor light conditions and cluttered scenes with significant levels of occlusion. An important requirement must be met: the system must exhibit a reasonable performance running on a low power consumption mobile GPU computing platform (NVIDIA Jetson TK1) so that it can be integrated in mobile robotics systems, ambient intelligence or ambient assisted living applications. The acquisition system is based on the use of color and depth (RGB-D) data streams provided by low-cost 3D sensors like Microsoft Kinect or PrimeSense Carmine. The range of algorithms and applications to be implemented and integrated will be quite broad, ranging from the acquisition, outlier removal or filtering of the input data and the segmentation or characterization of regions of interest in the scene to the very object recognition and pose estimation. Furthermore, in order to validate the proposed system, we will create a 3D object dataset. It will be composed by a set of 3D models, reconstructed from common household objects, as well as a handful of test scenes in which those objects appear. The scenes will be characterized by different levels of occlusion, diverse distances from the elements to the sensor and variations on the pose of the target objects. The creation of this dataset implies the additional development of 3D data acquisition and 3D object reconstruction applications. The resulting system has many possible applications, ranging from mobile robot navigation and semantic scene labeling to human-computer interaction (HCI) systems based on visual information

A Scalable Parallel Architecture with FPGA-Based Network Processor for Scientific Computing

This thesis discuss the design and the implementation of an FPGA-Based Network Processor for scientific computing, like Lattice Quantum ChromoDinamycs (LQCD) and fluid-dynamics applications based on Lattice Boltzmann Methods (LBM). State-of-the-art programs in this (and other similar) applications have a large degree of available parallelism, that can be easily exploited on massively parallel systems, provided the underlying communication network has not only high-bandwidth but also low-latency. I have designed in details, built and tested in hardware, firmware and software an implementation of a Network Processor, tailored for the most recent families of multi-core processors. The implementation has been developed on an FPGA device to easily interface the logic of NWP with the CPU I/O sub-system. In this work I have assessed several ways to move data between the main memory of the CPU and the I/O sub-system to exploit high data throughput and low latency, enabling the use of “Programmed Input Output” (PIO), “Direct Memory Access” (DMA) and “Write Combining” memory-settings. On the software side, I developed and test a device driver for the Linux operating system to access the NWP device, as well as a system library to efficiently access the network device from user-applications. This thesis demonstrates the feasibility of a network infrastructure that saturates the maximum bandwidth of the I/O sub-systems available on recent CPUs, and reduces communication latencies to values very close to those needed by the processor to move data across the chip boundary

Metagenome – Processing and Analysis

Metagenome means “multiple genomes” and the study of culture independent genomic content in environment is called metagenomics. Because of the advent of powerful and economic next generation sequencing technology, sequencing has become cheaper and faster and thus the study of genes and phenotypes is transitioning from single organism to that of a community present in the natural environmental sample. Once sequence data are obtained from an environmental sample, the challenge is to process, assemble and bin the metagenome data in order to get as accurate and complete a representation of the populations present in the community or to get high confident draft assembly. In this paper we describe the existing bioinformatics workflow to process the metagenomic data. Next, we examine one way of parallelizing the sequence similarity program on a High Performance Computing (HPC) cluster since sequence similarity is the most common and frequently used technique throughout the metagenome data processing and analyzing steps. In order to address the challenges involved in analyzing the result file obtained from sequence similarity program, we developed a web application tool called Contig Analysis Tool (CAT). Later, we applied the tools and techniques to the real world virome metagenomic data i.e., to the genomes of all the viruses present in the environmental sample obtained from microbial mats derived from hot springs in Yellowstone National Park. There are several challenges associated with the assembly and binning of virome data particularly because of the following reasons: 1. Not many viral sequence data in the existing databases for sequence similarity. 2. No reference genome 3. No phylogenetic marker genes like the ones present in the bacteria and archaea. We will see how we overcame these problems by performing sequence similarity using CRISPR data and sequence composition using tetranucleotide analysis

Visual Analysis Algorithms for Embedded Systems

Visual search systems are very popular applications, but on-line versions in 3G wireless environments suffer from network constraint like unstable or limited bandwidth that entail latency in query delivery, significantly degenerating the user’s experience. An alternative is to exploit the ability of the newest mobile devices to perform heterogeneous activities, like not only creating but also processing images. Visual feature extraction and compression can be performed on on-board Graphical Processing Units (GPUs), making smartphones capable of detecting a generic object (matching) in an exact way or of performing a classification activity. The latest trends in visual search have resulted in dedicated efforts in MPEG standardization, namely the MPEG CDVS (Compact Descriptor for Visual Search) standard. CDVS is an ISO/IEC standard used to extract a compressed descriptor. As regards to classification, in recent years neural networks have acquired an impressive importance and have been applied to several domains. This thesis focuses on the use of Deep Neural networks to classify images by means of Deep learning. Implementing visual search algorithms and deep learning-based classification on embedded environments is not a mere code-porting activity. Recent embedded devices are equipped with a powerful but limited number of resources, like development boards such as GPGPUs. GPU architectures fit particularly well, because they allow to execute more operations in parallel, following the SIMD (Single Instruction Multiple Data) paradigm. Nonetheless, it is necessary to make good design choices for the best use of available hardware and memory. For visual search, following the MPEG CDVS standard, the contribution of this thesis is an efficient feature computation phase, a parallel CDVS detector, completely implemented on embedded devices supporting the OpenCL framework. Algorithmic choices and implementation details to target the intrinsic characteristics of the selected embedded platforms are presented and discussed. Experimental results on several GPUs show that the GPU-based solution is up to 7× faster than the CPU-based one. This speed-up opens new visual search scenarios exploiting entire real-time on-board computations with no data transfer. As regards to the use of Deep convolutional neural networks for off-line image classification, their computational and memory requirements are huge, and this is an issue on embedded devices. Most of the complexity derives from the convolutional layers and in particular from the matrix multiplications they entail. The contribution of this thesis is a self-contained implementation to image classification providing common layers used in neural networks. The approach relies on a heterogeneous CPU-GPU scheme for performing convolutions in the transform domain. Experimental results show that the heterogeneous scheme described in this thesis boasts a 50× speedup over the CPU-only reference and outperforms a GPU-based reference by 2×, while slashing the power consumption by nearly 30%

Event-based Vision: A Survey

Event cameras are bio-inspired sensors that differ from conventional frame cameras: Instead of capturing images at a fixed rate, they asynchronously measure per-pixel brightness changes, and output a stream of events that encode the time, location and sign of the brightness changes. Event cameras offer attractive properties compared to traditional cameras: high temporal resolution (in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low power consumption, and high pixel bandwidth (on the order of kHz) resulting in reduced motion blur. Hence, event cameras have a large potential for robotics and computer vision in challenging scenarios for traditional cameras, such as low-latency, high speed, and high dynamic range. However, novel methods are required to process the unconventional output of these sensors in order to unlock their potential. This paper provides a comprehensive overview of the emerging field of event-based vision, with a focus on the applications and the algorithms developed to unlock the outstanding properties of event cameras. We present event cameras from their working principle, the actual sensors that are available and the tasks that they have been used for, from low-level vision (feature detection and tracking, optic flow, etc.) to high-level vision (reconstruction, segmentation, recognition). We also discuss the techniques developed to process events, including learning-based techniques, as well as specialized processors for these novel sensors, such as spiking neural networks. Additionally, we highlight the challenges that remain to be tackled and the opportunities that lie ahead in the search for a more efficient, bio-inspired way for machines to perceive and interact with the world