Algorithm and Hardware Co-Design for Local/Edge Computing

Advances in VLSI manufacturing and design technology over the decades have created many computing paradigms for disparate computing needs. With concerns for transmission cost, security, latency of centralized computing, edge/local computing are increasingly prevalent in the faster growing sectors like Internet-of-Things (IoT) and other sectors that require energy/connectivity autonomous systems such as biomedical and industrial applications. Energy and power efficient are the main design constraints in local and edge computing. While there exists a wide range of low power design techniques, they are often underutilized in custom circuit designs as the algorithms are developed independent of the hardware. Such compartmentalized design approach fails to take advantage of the many compatible algorithmic and hardware techniques that can improve the efficiency of the entire system. Algorithm hardware co-design is to explore the design space with whole stack awareness. The main goal of the algorithm hardware co-design methodology is the enablement and improvement of small form factor edge and local VLSI systems operating under strict constraints of area and energy efficiency. This thesis presents selected works of application specific digital and mixed-signal integrated circuit designs. The application space ranges from implantable biomedical devices to edge machine learning acceleration

Estimating XML Structural Join Size Quickly and Economically

XML structural joins, which evaluate the contain-ment (ancestor-descendant) relationships between XML el-ements, are important operations of XML query process-ing. Estimating structural join size accurately and quickly is thus crucial to the success of XML query plan selection and the query optimization. XML structural joins are essentially complex unequal joins, which render well-known estimation techniques, such as cosine transform, wavelet transform, and sketch, not directly applicable. In this paper, we pro-pose a relation model to capture the structural information of XML data such that the original complex unequal joins are converted to equal joins and those well-known estima-tion techniques become directly applicable to structural join size estimation. Theoretical analyses and extensive experi-ments have been performed on these estimation methods. It is shown that the cosine transform requires the least mem-ory and yields the best estimates.

A sampling approach for XML query selectivity estimation

As the Extensible Markup Language (XML) rapidly estab-lishes itself as the de facto standard for presenting, stor-ing, and exchanging data on the Internet, large volume of XML data and their supporting facilities start to surface. A fast and accurate selectivity estimation mechanism is of practical importance because selectivity estimation plays a fundamental role in XML query optimization. Recently pro-posed techniques are all based on some forms of structure synopses that could be time-consuming to build and not ef-fective for summarizing complex structure relationships. In this research, we propose an innovative sampling method that can capture the tree structures and intricate relation-ships among nodes in a simple and eective way. The de-rived sample tree is stored as a synopsis for selectivity esti-mation. Extensive experimental results show that, in com-parison with the state-of-the-art structure synopses, specif-ically the TreeSketch and Xseed synopses, our sample tree synopsis applies to a broader range of query types, requires several orders of magnitude less construction time, and gen-erates estimates with considerably better precision for com-plex datasets. 1

Efficient Processing of XML Twig Pattern: A Novel One-Phase Holistic Solution

Abstract. Modern twig query evaluation algorithms usually first generate individual path matches and then stitch them together (through a “merge ” operation) to form twig matches. In this paper, we propose a one-phase holistic twig evaluation algorithm based on the TwigStack algorithm. The proposed method applies a novel stack structure to preserve the holisticity of the twig matches. Without generating intermediate path matches, our method avoids the storage of individual path matches and the path merge process. Experimental results confirm the advantages of our approach.

Nanoscale Crystalline Sheets and Vesicles Assembled from Nonplanar Cyclic π-Conjugated Molecules

A fundamental challenge in chemistry and materials science is to create new carbon nanomaterials by assembling structurally unique carbon building blocks, such as nonplanar π-conjugated cyclic molecules. However, self-assembly of such cyclic π-molecules to form organized nanostructures has been rarely explored despite intensive studies on their chemical synthesis. Here we synthesized a family of new cycloparaphenylenes and found that these fully hydrophobic and nonplanar cyclic π-molecules could self-assemble into structurally distinct two-dimensional crystalline multilayer nanosheets. Moreover, these crystalline multilayer nanosheets could overcome inherent rigidity to curve into closed crystalline vesicles in solution. These supramolecular assemblies show that the cyclic molecular scaffolds are homogeneously arranged on the surface of nanosheets and vesicles with their molecular isotropic x-y plane standing obliquely on the surface. These supramolecular architectures that combined exact crystalline order, orientation-specific arrangement of π-conjugated cycles, controllable morphology, uniform molecular pore, superior florescence quench ability, and photoluminescence are expected to give rise to a new class of functional materials displaying unique photonic, electronic, and biological functions