Predicting program properties such as names or expression types has a wide range of applications. It can ease the task of programming, and increase programmer productivity. A major challenge when learning from programs is how to represent programs in a ...

A key challenge in program synthesis concerns how to efficiently search for the desired program in the space of possible programs. We propose a general approach to accelerate search-based program synthesis by biasing the search towards likely programs. ...

Bayesian Neural Networks (BNNs) have been proposed to address the problem of model uncertainty in training and inference. By introducing weights associated with conditioned probability distributions, BNNs are capable of resolving the overfitting issue ...

Many modern computing systems must provide reliable latency with minimal energy. Two central challenges arise when allocating system resources to meet these conflicting goals: (1) complexity modern hardware exposes diverse resources with complicated ...

Recent work on Winograd-based convolution allows for a great reduction of computational complexity, but existing implementations are limited to 2D data and a single kernel size of 3 by 3. They can achieve only slightly better, and often worse performance ...

This work presents a systematic exploration on the promise and special challenges of deep learning for sparse matrix format selection---a problem of determining the best storage format for a matrix to maximize the performance of Sparse Matrix Vector ...

Convolutional Neural Networks (CNN) are a class of Ar- tificial Neural Networks (ANN) that are highly efficient at the pattern recognition tasks that underlie difficult AI prob- lems in a variety of domains, such as speech recognition, object ...

We present a methodology for identifying security critical properties for use in the dynamic verification of a processor. Such verification has been shown to be an effective way to prevent exploits of vulnerabilities in the processor, given a meaningful ...

Availability of large data sets like ImageNet and massively parallel computation support in modern HPC devices like NVIDIA GPUs have fueled a renewed interest in Deep Learning (DL) algorithms. This has triggered the development of DL frameworks like ...

The current design trend in large scale machine learning is to use distributed clusters of CPUs and GPUs with MapReduce-style programming. Some have been led to believe that this type of horizontal scaling can reduce or even eliminate the need for ...

TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments. Its computational model is based on dataflow graphs with mutable state. Graph nodes may be mapped to different machines in a cluster, and within ...

In recent years the computing landscape has seen an increasing shift towards specialized accelerators. Field programmable gate arrays (FPGAs) are particularly promising for the implementation of these accelerators, as they offer significant performance ...

Code variants represent alternative implementations of a computation, and are common in high-performance libraries and applications to facilitate selecting the most appropriate implementation for a specific execution context (target architecture and ...

The Transactional Memory (TM) paradigm promises to greatly simplify the development of concurrent applications. This led, over the years, to the creation of a plethora of TM implementations delivering wide ranges of performance across workloads. Yet, no ...

In interactive services such as web search, recommendations, games and finance, reducing the tail latency is crucial to provide fast response to every user. Using web search as a driving example, we systematically characterize interactive workload to ...

Regression formulas are a domain-specific language adopted by several R packages for describing an important and useful class of statistical models: hierarchical linear regressions. Formulas are succinct, expressive, and clearly popular, so are they a ...

We propose algorithms for checking language equivalence of finite automata over a large alphabet. We use symbolic automata, where the transition function is compactly represented using (multi-terminal) binary decision diagrams (BDD). The key idea ...

We present an architecture designed to transparently and automatically scale the performance of sequential programs as a function of the hardware resources available. The architecture is predicated on a model of computation that views program execution ...

We propose a new kind of probabilistic programming language for machine learning. We write programs simply by annotating existing relational schemas with probabilistic model expressions. We describe a detailed design of our language, Tabular, complete ...

Symbolic Automata extend classical automata by using symbolic alphabets instead of finite ones. Most of the classical automata algorithms rely on the alphabet being finite, and generalizing them to the symbolic setting is not a trivial task. In this ...