vDNN: Virtualized Deep Neural Networks for Scalable, Memory-Efficient Neural Network Design

The most widely used machine learning frameworks require users to carefully tune their memory usage so that the deep neural network (DNN) fits into the DRAM capacity of a GPU. This restriction hampers a researcher's flexibility to study different machine learning algorithms, forcing them to either use a less desirable network architecture or parallelize the processing across multiple GPUs. We propose a runtime memory manager that virtualizes the memory usage of DNNs such that both GPU and CPU memory can simultaneously be utilized for training larger DNNs. Our virtualized DNN (vDNN) reduces the average GPU memory usage of AlexNet by up to 89%, OverFeat by 91%, and GoogLeNet by 95%, a significant reduction in memory requirements of DNNs. Similar experiments on VGG-16, one of the deepest and memory hungry DNNs to date, demonstrate the memory-efficiency of our proposal. vDNN enables VGG-16 with batch size 256 (requiring 28 GB of memory) to be trained on a single NVIDIA Titan X GPU card containing 12 GB of memory, with 18% performance loss compared to a hypothetical, oracular GPU with enough memory to hold the entire DNN.Comment: Published as a conference paper at the 49th IEEE/ACM International Symposium on Microarchitecture (MICRO-49), 201

Synthetic Multivalent Ligands as Probes of Signal Transduction

Cell-surface receptors acquire information from the extracellular environment and coordinate intracellular responses. Many receptors do not operate as individual entities, but rather as part of dimeric or oligomeric complexes. Coupling the functions of multiple receptors may endow signaling pathways with the sensitivity and malleability required to govern cellular responses. Moreover, multireceptor signaling complexes may provide a means of spatially segregating otherwise degenerate signaling cascades. Understanding the mechanisms, extent, and consequences of receptor co-localization and interreceptor communication is critical; chemical synthesis can provide compounds to address the role of receptor assembly in signal transduction. Multivalent ligands can be generated that possess a variety of sizes, shapes, valencies, orientations, and densities of binding elements. This Review focuses on the use of synthetic multivalent ligands to characterize receptor function.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/50669/1/2348_ftp.pd

Computer Architectures for Mobile Computer Vision Systems.

Mobile vision is enabling many new applications such as face recognition and augmented reality. However, the performance of mobile processors is limiting the capability of mobile vision computing. This dissertation presents an in-depth analysis of mobile computer vision applications and proposes novel hardware and software optimizations with the goal to increase mobile computer vision processing capability. We present the Michigan Visual Sonification System, a new mobile vision application that provides navigational aid to the visually impaired. The development of this application gives insights into the nature of mobile vision applications including the tradeoffs between performance and energy on mobile processors. We then present MEVBench, a mobile vision benchmark suite that we built to determine the computational characteristics of various mobile vision kernels. This analysis exposes the vector reduction operations, the imbalanced task or thread parallelism and the 2D spatial locality in memory accesses, all which we exploit in the pursuit of highly efficient mobile vision architectures. Armed with a deeper understanding of computer vision processing, the core of this thesis focuses on software and hardware based optimization to improve the efficiency of mobile vision processing. We begin the optimization with a software optimization known as Single Eigenvector Solver (SEVS), an algorithm that reduces the computation for augmented reality applications. We begin the hardware optimizations with EFFEX, a heterogeneous multicore architecture that utilizes vector reduction functional units and a 2D memory controller to improve the efficiency of feature extraction. We close with the Efficient Vision Architecture (EVA). EVA expands the EFFEX architecture by adding more custom accelerators for vision operations beyond feature extraction. It also utilizes the tile cache to allow for both 1D and 2D spatial locality in cache accesses. Overall, this dissertation demonstrates that an application specific approach to processor design can create a flexible programmable design with significant efficiency improvements in mobile vision performance when compared to currently available mobile processors. These works enable the development of richer more capable mobile vision systems.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/97782/1/jclemons_1.pd

Modeling the Evolution of Generativity and the Emergence of Digital Ecosystems

Recent literature on sociotechnical systems has employed the concept of generativity to explain the remarkable capacity for digital artifacts to support decentralized innovation and the emergence of rich business ecosystems. In this paper, we propose agent-based computational modeling as a tool for studying the evolution of generativity, and offer a set of building blocks for constructing agent-based models in which generativity evolves. We describe a series of models that we have created using these building blocks, and summarize the results of our computational experiments to date. We find in several different settings that key features of generative systems can themselves evolve endogenously, including core components and reusable parts. Moreover, we find that boundedly rational designers without coordination or foresight can evolve business ecosystems that satisfy a diverse range of consumer preferences and exhibit robustness to changes in these preferences over time. These findings present exciting opportunities for IS researchers