Natalie Parde

UNTANGLED: A Game Environment for Discovery of Creative Mapping Strategies

The problem of creating efficient mappings of dataflow graphs onto specific architectures (i.e., solving the place and route problem) is incredibly challenging. The difficulty is especially acute in the area of Coarse-Grained Reconfigurable Architectures (CGRAs) to the extent that solving the mapping problem may remove a significant bottleneck to adoption. We believe that the next generation of mapping algorithms will exhibit pattern recognition, the ability to learn from experience, and identification of creative solutions, all of which are human characteristics. This manuscript describes our game UNTANGLED, developed and fine-tuned over the course of a year to allow us to capture and analyze human mapping strategies. It also describes our results to date. We find that the mapping problem can be crowdsourced very effectively, that players can outperform existing algorithms, and that successful player strategies share many elements in common. Based on our observations and analysis, we make concrete recommendations for future research directions for mapping onto CGRAs.

UNTANGLED - An interactive mapping game for engineering education

Retaining students poses a huge challenge in the field of engineering, as many students become discouraged while working on their degrees and switch majors or leave school entirely. Our key observation is that it is extremely important to introduce students to real-world problems early on in their studies. Too often, students become confused and dissatisfied by abstract theories in their early engineering courses, and fail to see any practical importance to what they are learning. This paper presents the idea of using an interactive game, UNTANGLED, to introduce real-world problems related to chip architecture and design in the early stages of engineering education, thus generating enthusiasm and helping students connect the theories they learn in classes to applicable problems. We believe that doing so will help elevate future engineering student retention rates.

Towards a Top-down Policy Engineering Framework for Attribute-based Access Control

Attribute-based access control (ABAC) is a logical access control methodology where authorization to perform a set of operations is based on attributes of the user, the objects being accessed, the environment, and a number of other attribute sources that may be relevant to the current request. Once fully implemented within an enterprise, ABAC promotes information sharing while maintaining control of the information. However, the cost of developing ABAC policies can be a significant obstacle for organizations to migrate from traditional access control models to ABAC. Most organizations have high-level requirement specifications that define security policies and include a set of access control policies. Taking advantage of this rich source of information, we introduce a top-down policy engineering framework for ABAC that aims to automatically extract policies from unrestricted natural language documents and then, we present our methodology to extract policy related information using deep neural networks. We first create an annotated dataset comprised of 2660 sentences from real-world policy documents. We then train a deep recurrent neural network (RNN) to identify sentences containing access control policies (ACP) from irrelevant content. We applied the RNN to our new dataset as well as to five other, smaller datasets that have been employed in prior work on this task, and show that our model outperforms the state-of-the-art and leads to a performance improvement of 5.58% over the previously reported results.

Data-Driven Mapping Using Local Patterns

The problem of mapping a data flow graph onto a reconfigurable architecture has been difficult to solve quickly and optimally. Anytime algorithms have the potential to meet both goals by generating a good solution quickly and improving that solution over time, but they have not been shown to be practical for mapping. The key insight into this paper is that mapping algorithms based on search trees can be accelerated using a database of examples of high quality mappings. The depth of the search tree is reduced by placing patterns of nodes rather than single nodes at each level. The branching factor is reduced by placing patterns only in arrangements present in a dictionary constructed from examples. We present two anytime algorithms that make use of patterns and dictionaries: Anytime A* and Anytime Multiline Tree Rollup. We compare these algorithms to simulated annealing and to results from human mappers playing the online game UNTANGLED. The anytime algorithms outperform simulated annealing and the best game players in the majority of cases, and the combined results from all algorithms provide an informative comparison between architecture choices.

An interactive framework for learning user-object associations through human-robot interaction

A great deal of recent research has focused on social and assistive robots that can achieve a more natural and realistic interaction between the agent and its environment. Following this direction, this paper aims to establish a computational framework that can associate objects with their uses and their basic characteristics in an automated manner. The goal is to continually enrich the robots knowledge regarding objects that are important to the user, through verbal interaction. We address the problem of learning correlations between object properties and human needs by associating visual with verbal information. Although the visual information can be acquired directly by the robot, the verbal information is acquired via interaction with a human user. Users provide descriptions of the objects for which the robot has captured visual information, and these two sources of information are combined automatically. We present a general model for learning these associations using Gaussian Mixture Models. Since learning is based on a probabilistic model, the approach handles uncertainty, redundancy, and irrelevant information. We illustrate the capabilities of our approach by presenting the results of an initial experiment run in a laboratory environment, and we describe the set of modules that support the proposed framework.

Using EEG Signal to Analyze IS Decision Making Cognitive Processes

In this paper, we demonstrate how electroencephalograph (EEG) signals can be used to analyze people’s mental states while engaging in cognitive processes during IS decision-making. We design an experiment in which participants are required to complete several cognitive tasks with various cognitive demands and under various stress levels. We collect their EEG signals as they perform the tasks and analyze those signals to infer their mental state (e.g., relaxation level and stress level) based on their EEG signal power.

Cross-Architectural Study of Custom Reconfigurable Devices Using Crowdsourcing

Coarse grained reconfigurable architectures (CGRAs) are promising due to the ability to highly customize such architectures to an application domain. However, good tools and good algorithms to map benchmarks onto these architectures are needed to support design space exploration for CGRAs. In particular, the mapping problem has been difficult to solve in a satisfying and general way. In this paper, we present an architectural design flow using crowd sourcing to provide mappings of benchmarks onto new architectures. We show that the crowd can provide high quality, reliable mappings, outperforming our custom Simulated Annealing algorithm in 37 of 42 trials. We further show that the crowd can provide other types of feedback that are difficult to obtain from an automatic mapping algorithm. Our proof of concept cross-architectural study concludes that a mesh architecture with 8Way connectivity outperforms the other interconnection options tested. A stripe architecture with dedicated vertical routes (StripeDR) performs competitively as well.