Literature Review of Deep Network Compression

Deep networks often possess a vast number of parameters, and their significant redundancy in parameterization has become a widely-recognized property. This presents significant challenges and restricts many deep learning applications, making the focus on reducing the complexity of models while maintaining their powerful performance. In this paper, we present an overview of popular methods and review recent works on compressing and accelerating deep neural networks. We consider not only pruning methods but also quantization methods, and low-rank factorization methods. This review also intends to clarify these major concepts, and highlights their characteristics, advantages, and shortcomings

Performance Modeling of Inline Compression With Software Caching for Reducing the Memory Footprint in PYSDC

Modern HPC applications compute and analyze massive amounts of data. The data volume is growing faster than memory capabilities and storage improvements leading to performance bottlenecks. An example of this is pySDC, a framework for solving collocation problems iteratively using parallel-in-time methods. These methods require storing and exchanging 3D volume data for each parallel point in time. If a simulation consists of M parallel-in-time stages, where the full spatial problem has to be stored for the next iteration, the memory demand for a single state variable is M ×Nx ×Ny ×Nz per time-step. For an application simulation with many state variables or stages, the memory requirement is considerable. Data compression helps alleviate the overhead in memory by reducing the size of data and keeping it in compressed format. Inline compression compresses and decompresses the application’s working set as it moves in and out of main memory. Thus, it provides the system with the appearance of more main memory. Naive compressed arrays require a compression or decompression operation for each store or load and therefore hurt the performance of the application. By incorporating a software cache and storing decompressed values of the array, we limit the number of compression and decompression operations for the stores and loads, thereby improving performance overall. In this thesis, we build a compression manager and software cache manager for the pySDC framework to reduce the memory requirements and computational overhead. The compression manager wraps around LibPressio, a C++ compression library that abstracts all compressors. We utilize blosc, a lossless compressor for our compression manager, and build a software cache manager with various cache configurations and cache policies to work in cohesion with the compression manager. We build a performance model which evaluates the compression manager and cache manager’s performance on different metrics such as compression ratio and compression/decompression time. We test our framework on two different pySDC applications — e.g., Allen-Cahn and Heat-diffusion. ii Results show that incorporating compression and increasing the cache size for our applications inflates the total compressed size in bytes for the arrays and therefore reduces the compression ratio, in contrast to our expectations. However, incorporating the cache and a greater cache size reduces the number of compression/decompression calls to LibPressio as well as cache evictions, significantly reducing the computational overhead for pySDC. Thus, overall, our compression and cache manager help reduce the memory footprint in pySDC. Future work involves looking at improving the compression ratio and using lossy compression to achieve significant reduction in memory footprint

3D multiresolution statistical approaches for accelerated medical image and volume segmentation

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Medical volume segmentation got the attraction of many researchers; therefore, many techniques have been implemented in terms of medical imaging including segmentations and other imaging processes. This research focuses on an implementation of segmentation system which uses several techniques together or on their own to segment medical volumes, the system takes a stack of 2D slices or a full 3D volumes acquired from medical scanners as a data input. Two main approaches have been implemented in this research for segmenting medical volume which are multi-resolution analysis and statistical modeling. Multi-resolution analysis has been mainly employed in this research for extracting the features. Higher dimensions of discontinuity (line or curve singularity) have been extracted in medical images using a modified multi-resolution analysis transforms such as ridgelet and curvelet transforms. The second implemented approach in this thesis is the use of statistical modeling in medical image segmentation; Hidden Markov models have been enhanced here to segment medical slices automatically, accurately, reliably and with lossless results. But the problem with using Markov models here is the computational time which is too long. This has been addressed by using feature reduction techniques which has also been implemented in this thesis. Some feature reduction and dimensionality reduction techniques have been used to accelerate the slowest block in the proposed system. This includes Principle Components Analysis, Gaussian Pyramids and other methods. The feature reduction techniques have been employed efficiently with the 3D volume segmentation techniques such as 3D wavelet and 3D Hidden Markov models. The system has been tested and validated using several procedures starting at a comparison with the predefined results, crossing the specialists’ validations, and ending by validating the system using a survey filled by the end users explaining the techniques and the results. This concludes that Markovian models segmentation results has overcome all other techniques in most patients’ cases. Curvelet transform has been also proved promising segmentation results; the end users rate it better than Markovian models due to the long time required with Hidden Markov models

Review and classification of trajectory summarisation algorithms: From compression to segmentation

With the continuous development and cost reduction of positioning and tracking technologies, a large amount of trajectories are being exploited in multiple domains for knowledge extraction. A trajectory is formed by a large number of measurements, where many of them are unnecessary to describe the actual trajectory of the vehicle, or even harmful due to sensor noise. This not only consumes large amounts of memory, but also makes the extracting knowledge process more difficult. Trajectory summarisation techniques can solve this problem, generating a smaller and more manageable representation and even semantic segments. In this comprehensive review, we explain and classify techniques for the summarisation of trajectories according to their search strategy and point evaluation criteria, describing connections with the line simplification problem. We also explain several special concepts in trajectory summarisation problem. Finally, we outline the recent trends and best practices to continue the research in next summarisation algorithms.The author(s) disclosed receipt of the following financial support for the research, authorship and/or publication of this article: This work was funded by public research projects of Spanish Ministry of Economy and Competitivity (MINECO), reference TEC2017-88048-C2-2-