186 research outputs found
Doctor of Philosophy
dissertationThe embedded system space is characterized by a rapid evolution in the complexity and functionality of applications. In addition, the short time-to-market nature of the business motivates the use of programmable devices capable of meeting the conflicting constraints of low-energy, high-performance, and short design times. The keys to achieving these conflicting constraints are specialization and maximally extracting available application parallelism. General purpose processors are flexible but are either too power hungry or lack the necessary performance. Application-specific integrated circuits (ASICS) efficiently meet the performance and power needs but are inflexible. Programmable domain-specific architectures (DSAs) are an attractive middle ground, but their design requires significant time, resources, and expertise in a variety of specialties, which range from application algorithms to architecture and ultimately, circuit design. This dissertation presents CoGenE, a design framework that automates the design of energy-performance-optimal DSAs for embedded systems. For a given application domain and a user-chosen initial architectural specification, CoGenE consists of a a Compiler to generate execution binary, a simulator Generator to collect performance/energy statistics, and an Explorer that modifies the current architecture to improve energy-performance-area characteristics. The above process repeats automatically until the user-specified constraints are achieved. This removes or alleviates the time needed to understand the application, manually design the DSA, and generate object code for the DSA. Thus, CoGenE is a new design methodology that represents a significant improvement in performance, energy dissipation, design time, and resources. This dissertation employs the face recognition domain to showcase a flexible architectural design methodology that creates "ASIC-like" DSAs. The DSAs are instruction set architecture (ISA)-independent and achieve good energy-performance characteristics by coscheduling the often conflicting constraints of data access, data movement, and computation through a flexible interconnect. This represents a significant increase in programming complexity and code generation time. To address this problem, the CoGenE compiler employs integer linear programming (ILP)-based 'interconnect-aware' scheduling techniques for automatic code generation. The CoGenE explorer employs an iterative technique to search the complete design space and select a set of energy-performance-optimal candidates. When compared to manual designs, results demonstrate that CoGenE produces superior designs for three application domains: face recognition, speech recognition and wireless telephony. While CoGenE is well suited to applications that exhibit a streaming behavior, multithreaded applications like ray tracing present a different but important challenge. To demonstrate its generality, CoGenE is evaluated in designing a novel multicore N-wide SIMD architecture, known as StreamRay, for the ray tracing domain. CoGenE is used to synthesize the SIMD execution cores, the compiler that generates the application binary, and the interconnection subsystem. Further, separating address and data computations in space reduces data movement and contention for resources, thereby significantly improving performance compared to existing ray tracing approaches
SurfNet: Generating 3D shape surfaces using deep residual networks
3D shape models are naturally parameterized using vertices and faces, \ie,
composed of polygons forming a surface. However, current 3D learning paradigms
for predictive and generative tasks using convolutional neural networks focus
on a voxelized representation of the object. Lifting convolution operators from
the traditional 2D to 3D results in high computational overhead with little
additional benefit as most of the geometry information is contained on the
surface boundary. Here we study the problem of directly generating the 3D shape
surface of rigid and non-rigid shapes using deep convolutional neural networks.
We develop a procedure to create consistent `geometry images' representing the
shape surface of a category of 3D objects. We then use this consistent
representation for category-specific shape surface generation from a parametric
representation or an image by developing novel extensions of deep residual
networks for the task of geometry image generation. Our experiments indicate
that our network learns a meaningful representation of shape surfaces allowing
it to interpolate between shape orientations and poses, invent new shape
surfaces and reconstruct 3D shape surfaces from previously unseen images.Comment: CVPR 2017 pape
Microarchitectural techniques to reduce interconnect power in clustered processors
Journal ArticleThe paper presents a preliminary evaluation of novel techniques that address a growing problem - power dissipation in on-chip interconnects. Recent studies have shown that around 50% of the dynamic power consumption in modern processors is within on-chip interconnects. The contribution of interconnect power to total chip power is expected to be higher in future communication-bound billion-transistor architectures. In this paper, we propose the design of a heterogeneous interconnect, where some wires are optimized for low latency and others are optimized for low power. We show that a large fraction of on-chip communications are latency insensitive. Effecting these non-critical transfers on low-power long-latency interconnects can result in significant power savings without unduly affecting performance. Two primary techniques are evaluated in this paper: (i) a dynamic critical path predictor that identifies results that are not urgently consumed, and (ii) an address prediction mechanism that requires addresses to be transferred off the critical path for verification purposes. Our results demonstrate that 49% of all interconnect transfers can be effected on power-efficient wires, while incurring a performance penalty of only 2.5%
Deep Ritz Method with Adaptive Quadrature for Linear Elasticity
In this paper, we study the deep Ritz method for solving the linear
elasticity equation from a numerical analysis perspective. A modified Ritz
formulation using the norm is introduced and analyzed for
linear elasticity equation in order to deal with the (essential) Dirichlet
boundary condition. We show that the resulting deep Ritz method provides the
best approximation among the set of deep neural network (DNN) functions with
respect to the ``energy'' norm. Furthermore, we demonstrate that the total
error of the deep Ritz simulation is bounded by the sum of the network
approximation error and the numerical integration error, disregarding the
algebraic error. To effectively control the numerical integration error, we
propose an adaptive quadrature-based numerical integration technique with a
residual-based local error indicator. This approach enables efficient
approximation of the modified energy functional. Through numerical experiments
involving smooth and singular problems, as well as problems with stress
concentration, we validate the effectiveness and efficiency of the proposed
deep Ritz method with adaptive quadrature
Three dimensional shape comparison of flexible proteins using the local-diameter descriptor
<p>Abstract</p> <p>Background</p> <p>Techniques for inferring the functions of the protein by comparing their shape similarity have been receiving a lot of attention. Proteins are functional units and their shape flexibility occupies an essential role in various biological processes. Several shape descriptors have demonstrated the capability of protein shape comparison by treating them as rigid bodies. But this may give rise to an incorrect comparison of flexible protein shapes.</p> <p>Results</p> <p>We introduce an efficient approach for comparing flexible protein shapes by adapting a <it>local diameter </it>(LD) <it>descriptor</it>. The LD descriptor, developed recently to handle skeleton based shape deformations <abbrgrp><abbr bid="B1">1</abbr></abbrgrp>, is adapted in this work to capture the invariant properties of shape deformations caused by the motion of the protein backbone. Every sampled point on the protein surface is assigned a value measuring the diameter of the 3D shape in the neighborhood of that point. The LD descriptor is built in the form of a one dimensional histogram from the distribution of the diameter values. The histogram based shape representation reduces the shape comparison problem of the flexible protein to a simple distance calculation between 1D feature vectors. Experimental results indicate how the LD descriptor accurately treats the protein shape deformation. In addition, we use the LD descriptor for protein shape retrieval and compare it to the effectiveness of conventional shape descriptors. A sensitivity-specificity plot shows that the LD descriptor performs much better than the conventional shape descriptors in terms of consistency over a family of proteins and discernibility across families of different proteins.</p> <p>Conclusion</p> <p>Our study provides an effective technique for comparing the shape of flexible proteins. The experimental results demonstrate the insensitivity of the LD descriptor to protein shape deformation. The proposed method will be potentially useful for molecule retrieval with similar shapes and rapid structure retrieval for proteins. The demos and supplemental materials are available on <url>https://engineering.purdue.edu/PRECISE/LDD</url>.</p
DETC2005-85314 AN APPROACH TO DRAWING-LIKE VIEW GENERATION FROM 3D MODELS
ABSTRACT In this paper we propose a method to generate 2D drawinglike views from 3D models automatically. The view generation process is conducted in object space and supported by two algorithms: (1) pose determination for 3D models: unifying the space between 2D drawings and 3D models; and (2) 2D drawing-like view generation from 3D models: building the correspondence between 2D drawings and 3D models. The pose determination method for 3D objects is proposed on the basis of a concept called Virtual Contact Area. Meanwhile an efficient occlusion algorithm based regular grid is described to generate orthogonal drawing-like views from 3D models along the pose orientations. To evaluate the validity of the proposed methods, respective experiments are presented. INTRODUCTION As two different ways to express and communicate design ideas, 2D drawings and 3D models are now being widely used in many fields. For designers with special skills, 2D drawings are usually used as the principal way to express ideas; while for most common users, 3D models are more intuitive than 2D drawings from the perspective of information communication. How to seamlessly transit between the two representations is a public problem for many researchers in engineering fields. In spite of the fact that many methods To find the optimal orientations for a 3D model, Park et al. [4] used a pose determination technique to integrate tw
An HCI-Centric Survey and Taxonomy of Human-Generative-AI Interactions
Generative AI (GenAI) has shown remarkable capabilities in generating diverse
and realistic content across different formats like images, videos, and text.
In Generative AI, human involvement is essential, thus HCI literature has
investigated how to effectively create collaborations between humans and GenAI
systems. However, the current literature lacks a comprehensive framework to
better understand Human-GenAI Interactions, as the holistic aspects of
human-centered GenAI systems are rarely analyzed systematically. In this paper,
we present a survey of 291 papers, providing a novel taxonomy and analysis of
Human-GenAI Interactions from both human and Gen-AI perspectives. The
dimensions of design space include 1) Purposes of Using Generative AI, 2)
Feedback from Models to Users, 3) Control from Users to Models, 4) Levels of
Engagement, 5) Application Domains, and 6) Evaluation Strategies. Our work is
also timely at the current development stage of GenAI, where the Human-GenAI
interaction design is of paramount importance. We also highlight challenges and
opportunities to guide the design of Gen-AI systems and interactions towards
the future design of human-centered Generative AI applications
3DMolNavi: A Web-Based Retrieval and Navigation Tool for Flexible Molecular Shape Comparison.
Background
Many molecules of interest are flexible and undergo significant shape deformation as part of their function, but most existing methods of molecular shape comparison treat them as rigid shapes, which may lead to incorrect measure of the shape similarity of flexible molecules. Currently, there still is a limited effort in retrieval and navigation for flexible molecular shape comparison, which would improve data retrieval by helping users locate the desirable molecule in a convenient way. Results
To address this issue, we develop a web-based retrieval and navigation tool, named 3DMolNavi, for flexible molecular shape comparison. This tool is based on the histogram of Inner Distance Shape Signature (IDSS) for fast retrieving molecules that are similar to a query molecule, and uses dimensionality reduction to navigate the retrieved results in 2D and 3D spaces. We tested 3DMolNavi in the Database of Macromolecular Movements (MolMovDB) and CATH. Compared to other shape descriptors, it achieves good performance and retrieval results for different classes of flexible molecules. Conclusions
The advantages of 3DMolNavi, over other existing softwares, are to integrate retrieval for flexible molecular shape comparison and enhance navigation for user’s interaction. 3DMolNavi can be accessed via https://engineering.purdue.edu/PRECISE/3dmolnavi/index.html webcite
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