13 research outputs found
Physical Representation-based Predicate Optimization for a Visual Analytics Database
Querying the content of images, video, and other non-textual data sources
requires expensive content extraction methods. Modern extraction techniques are
based on deep convolutional neural networks (CNNs) and can classify objects
within images with astounding accuracy. Unfortunately, these methods are slow:
processing a single image can take about 10 milliseconds on modern GPU-based
hardware. As massive video libraries become ubiquitous, running a content-based
query over millions of video frames is prohibitive.
One promising approach to reduce the runtime cost of queries of visual
content is to use a hierarchical model, such as a cascade, where simple cases
are handled by an inexpensive classifier. Prior work has sought to design
cascades that optimize the computational cost of inference by, for example,
using smaller CNNs. However, we observe that there are critical factors besides
the inference time that dramatically impact the overall query time. Notably, by
treating the physical representation of the input image as part of our query
optimization---that is, by including image transforms, such as resolution
scaling or color-depth reduction, within the cascade---we can optimize data
handling costs and enable drastically more efficient classifier cascades.
In this paper, we propose Tahoma, which generates and evaluates many
potential classifier cascades that jointly optimize the CNN architecture and
input data representation. Our experiments on a subset of ImageNet show that
Tahoma's input transformations speed up cascades by up to 35 times. We also
find up to a 98x speedup over the ResNet50 classifier with no loss in accuracy,
and a 280x speedup if some accuracy is sacrificed.Comment: Camera-ready version of the paper submitted to ICDE 2019, In
Proceedings of the 35th IEEE International Conference on Data Engineering
(ICDE 2019
BlinkML: Efficient Maximum Likelihood Estimation with Probabilistic Guarantees
The rising volume of datasets has made training machine learning (ML) models
a major computational cost in the enterprise. Given the iterative nature of
model and parameter tuning, many analysts use a small sample of their entire
data during their initial stage of analysis to make quick decisions (e.g., what
features or hyperparameters to use) and use the entire dataset only in later
stages (i.e., when they have converged to a specific model). This sampling,
however, is performed in an ad-hoc fashion. Most practitioners cannot precisely
capture the effect of sampling on the quality of their model, and eventually on
their decision-making process during the tuning phase. Moreover, without
systematic support for sampling operators, many optimizations and reuse
opportunities are lost.
In this paper, we introduce BlinkML, a system for fast, quality-guaranteed ML
training. BlinkML allows users to make error-computation tradeoffs: instead of
training a model on their full data (i.e., full model), BlinkML can quickly
train an approximate model with quality guarantees using a sample. The quality
guarantees ensure that, with high probability, the approximate model makes the
same predictions as the full model. BlinkML currently supports any ML model
that relies on maximum likelihood estimation (MLE), which includes Generalized
Linear Models (e.g., linear regression, logistic regression, max entropy
classifier, Poisson regression) as well as PPCA (Probabilistic Principal
Component Analysis). Our experiments show that BlinkML can speed up the
training of large-scale ML tasks by 6.26x-629x while guaranteeing the same
predictions, with 95% probability, as the full model.Comment: 22 pages, SIGMOD 201
Modelo de reconocimiento de variables que influyen en el rendimiento de transacciones de RDBMS aplicando PCA y PCR
El conocimiento de los factores que influencian la eficiencia de un sistema es importante para su correcta administración y mantenimiento. Múltiples organizaciones soportan sus operaciones con aplicaciones que interactúan con un sistema de gestión de bases de datos relacional (RDBMS), las cuales pueden mejorar su eficiencia a través del conocimiento de los factores que influyen en el rendimiento de las ejecuciones de las sentencias SQL que conforman sus cargas de trabajo, especialmente las cargas de trabajo generadas por aplicaciones implementadas en ambientes de producción que manifiestan recurrencia en el tiempo. El artÃculo de investigación que se presenta propone un modelo de reconocimiento de factores que afectan el rendimiento de las ejecuciones de las sentencias SQL que se procesan en un RDBMS y discute la implementación de una técnica de predicción de métrica de rendimiento, valiéndose de los algoritmos de aprendizaje de máquina estadÃstico denominados Análisis de Componentes Principales (PCA) y Regresión de Componentes Principales (PCR), que explotan la información de los planes, estadÃsticas y métricas generadas durante el ciclo de vida de las ejecuciones de las sentencias SQL
Approximate Computing Survey, Part I: Terminology and Software & Hardware Approximation Techniques
The rapid growth of demanding applications in domains applying multimedia
processing and machine learning has marked a new era for edge and cloud
computing. These applications involve massive data and compute-intensive tasks,
and thus, typical computing paradigms in embedded systems and data centers are
stressed to meet the worldwide demand for high performance. Concurrently, the
landscape of the semiconductor field in the last 15 years has constituted power
as a first-class design concern. As a result, the community of computing
systems is forced to find alternative design approaches to facilitate
high-performance and/or power-efficient computing. Among the examined
solutions, Approximate Computing has attracted an ever-increasing interest,
with research works applying approximations across the entire traditional
computing stack, i.e., at software, hardware, and architectural levels. Over
the last decade, there is a plethora of approximation techniques in software
(programs, frameworks, compilers, runtimes, languages), hardware (circuits,
accelerators), and architectures (processors, memories). The current article is
Part I of our comprehensive survey on Approximate Computing, and it reviews its
motivation, terminology and principles, as well it classifies and presents the
technical details of the state-of-the-art software and hardware approximation
techniques.Comment: Under Review at ACM Computing Survey
Approximate Computing Survey, Part II: Application-Specific & Architectural Approximation Techniques and Applications
The challenging deployment of compute-intensive applications from domains
such Artificial Intelligence (AI) and Digital Signal Processing (DSP), forces
the community of computing systems to explore new design approaches.
Approximate Computing appears as an emerging solution, allowing to tune the
quality of results in the design of a system in order to improve the energy
efficiency and/or performance. This radical paradigm shift has attracted
interest from both academia and industry, resulting in significant research on
approximation techniques and methodologies at different design layers (from
system down to integrated circuits). Motivated by the wide appeal of
Approximate Computing over the last 10 years, we conduct a two-part survey to
cover key aspects (e.g., terminology and applications) and review the
state-of-the art approximation techniques from all layers of the traditional
computing stack. In Part II of our survey, we classify and present the
technical details of application-specific and architectural approximation
techniques, which both target the design of resource-efficient
processors/accelerators & systems. Moreover, we present a detailed analysis of
the application spectrum of Approximate Computing and discuss open challenges
and future directions.Comment: Under Review at ACM Computing Survey
Benign Autoencoders
The success of modern machine learning algorithms depends crucially on
efficient data representation and compression through dimensionality reduction.
This practice seemingly contradicts the conventional intuition suggesting that
data processing always leads to information loss. We prove that this intuition
is wrong. For any non-convex problem, there exists an optimal, benign
auto-encoder (BAE) extracting a lower-dimensional data representation that is
strictly beneficial: Compressing model inputs improves model performance. We
prove that BAE projects data onto a manifold whose dimension is the
compressibility dimension of the learning model. We develop and implement an
efficient algorithm for computing BAE and show that BAE improves model
performance in every dataset we consider. Furthermore, by compressing
"malignant" data dimensions, BAE makes learning more stable and robust.Comment: This paper replaces and subsumes arXiv:2110.0888
Runtime Support for Human-in-the-Loop Feature Engineering Systems
Abstract A machine learning system is only as good as its feature