LCNN: Lookup-based Convolutional Neural Network

Porting state of the art deep learning algorithms to resource constrained compute platforms (e.g. VR, AR, wearables) is extremely challenging. We propose a fast, compact, and accurate model for convolutional neural networks that enables efficient learning and inference. We introduce LCNN, a lookup-based convolutional neural network that encodes convolutions by few lookups to a dictionary that is trained to cover the space of weights in CNNs. Training LCNN involves jointly learning a dictionary and a small set of linear combinations. The size of the dictionary naturally traces a spectrum of trade-offs between efficiency and accuracy. Our experimental results on ImageNet challenge show that LCNN can offer 3.2x speedup while achieving 55.1% top-1 accuracy using AlexNet architecture. Our fastest LCNN offers 37.6x speed up over AlexNet while maintaining 44.3% top-1 accuracy. LCNN not only offers dramatic speed ups at inference, but it also enables efficient training. In this paper, we show the benefits of LCNN in few-shot learning and few-iteration learning, two crucial aspects of on-device training of deep learning models.Comment: CVPR 1

A Case Study of Using Domain Analysis for the Conflation Algorithms Domain

This paper documents the domain engineering process for much of the conflation algorithms domain. Empirical data on the process and products of domain engineering were collected. Six conflation algorithms of four different types: three affix removal, one successor variety, one table lookup, and one n-gram were analyzed. Products of the analysis include a generic architecture, reusable components, a little language and an application generator that extends the scope of the domain analysis beyond previous generators. The application generator produces source code for not only affix removal type but also successor variety, table lookup, and n-gram stemmers. The performance of the stemmers generated automatically was compared with the stemmers developed manually in terms of stem similarity, source and executable sizes, and development and execution times. All five stemmers generated by the application generator produced more than 99.9% identical stems with the manually developed stemmers. Some of the generated stemmers were as efficient as their manual equivalents and some were not

ROOT, an object oriented data analysis framework

ROOT is an object-oriented framework aimed at solving the data analysis challenges of high-energy physics. Here we discuss the main components of the framework. We begin with an overview describing the framework's organization, the interpreter CINT, its automatic interface to the compiler and linker ACLiC, and an example of a first interactive session. The subsequent sections cover histogramming and fitting. Then, ROOT's solution to storing and retrieving HEP data, building and managing of ROOT files, and designing ROOT trees. Followed by a description of the collection classes, the GUI classes, how to add your own classes to ROOT, and PROOF, ROOT's parallel processing facility

REAL-TIME COMPRESSION OF SOFTWARE TRACES

Techniques are presented herein that support the compression of software-generated traces as a stream, in real time, with reduced central processing unit (CPU) overhead. Such an approach may reduce cloud hosting bandwidth charges and is relevant when moving troubleshooting information from a device into the cloud for analysis. Additionally, such an approach eliminates the bursty nature of file-based compression that is typically achieved using legacy compression utilities. As a result, the presented techniques are more amenable to small CPU footprints such as, for example, a cloud-based router having just a single CPU. Aspects of the presented techniques have a broad scope and may be applied to any software system that generates traces, which is typically all modern software systems. Further aspects of the presented techniques may potentially be applied to industry technologies (such as OpenTelemetry) that support the distributed tracing of cloud hosted applications

Handling Massive N-Gram Datasets Efficiently

This paper deals with the two fundamental problems concerning the handling of large n-gram language models: indexing, that is compressing the n-gram strings and associated satellite data without compromising their retrieval speed; and estimation, that is computing the probability distribution of the strings from a large textual source. Regarding the problem of indexing, we describe compressed, exact and lossless data structures that achieve, at the same time, high space reductions and no time degradation with respect to state-of-the-art solutions and related software packages. In particular, we present a compressed trie data structure in which each word following a context of fixed length k, i.e., its preceding k words, is encoded as an integer whose value is proportional to the number of words that follow such context. Since the number of words following a given context is typically very small in natural languages, we lower the space of representation to compression levels that were never achieved before. Despite the significant savings in space, our technique introduces a negligible penalty at query time. Regarding the problem of estimation, we present a novel algorithm for estimating modified Kneser-Ney language models, that have emerged as the de-facto choice for language modeling in both academia and industry, thanks to their relatively low perplexity performance. Estimating such models from large textual sources poses the challenge of devising algorithms that make a parsimonious use of the disk. The state-of-the-art algorithm uses three sorting steps in external memory: we show an improved construction that requires only one sorting step thanks to exploiting the properties of the extracted n-gram strings. With an extensive experimental analysis performed on billions of n-grams, we show an average improvement of 4.5X on the total running time of the state-of-the-art approach.Comment: Published in ACM Transactions on Information Systems (TOIS), February 2019, Article No: 2

A fast compression-based similarity measure with applications to content-based image retrieval

Compression-based similarity measures are effectively employed in applications on diverse data types with a basically parameter-free approach. Nevertheless, there are problems in applying these techniques to medium-to-large datasets which have been seldom addressed. This paper proposes a similarity measure based on compression with dictionaries, the Fast Compression Distance (FCD), which reduces the complexity of these methods, without degradations in performance. On its basis a content-based color image retrieval system is defined, which can be compared to state-of-the-art methods based on invariant color features. Through the FCD a better understanding of compression-based techniques is achieved, by performing experiments on datasets which are larger than the ones analyzed so far in literature