SEER: Super-Optimization Explorer for HLS using E-graph Rewriting with MLIR

High-level synthesis (HLS) is a process that automatically translates a software program in a high-level language into a low-level hardware description. However, the hardware designs produced by HLS tools still suffer from a significant performance gap compared to manual implementations. This is because the input HLS programs must still be written using hardware design principles. Existing techniques either leave the program source unchanged or perform a fixed sequence of source transformation passes, potentially missing opportunities to find the optimal design. We propose a super-optimization approach for HLS that automatically rewrites an arbitrary software program into efficient HLS code that can be used to generate an optimized hardware design. We developed a toolflow named SEER, based on the e-graph data structure, to efficiently explore equivalent implementations of a program at scale. SEER provides an extensible framework, orchestrating existing software compiler passes and hardware synthesis optimizers. Our work is the first attempt to exploit e-graph rewriting for large software compiler frameworks, such as MLIR. Across a set of open-source benchmarks, we show that SEER achieves up to 38x the performance within 1.4x the area of the original program. Via an Intel-provided case study, SEER demonstrates the potential to outperform manually optimized designs produced by hardware experts

Table Augmentation in Data Lakes

Data lakes are centralized repositories that store large quantities of raw, unstructured, and structured data, allowing for ad-hoc data analysis, exploratory data analysis, and machine learning. However, the lack of metadata and schema in data lakes makes it challenging to work with tabular data and find related information stored in different tables. However, it is still an open problem how efficiently retrieve these tables at large scale when the settings of a data lake holds. The thesis introduces a novel approach to table augmentation that enables efficient data integration from multiple sources in a data lake. Table augmentation involves adding new data to an existing table in a horizontal fashion (by retrieving tables that can be horizontally concatenated to a query that serves as query table). The proposed approach consists of several components, including data lakes hashing, join search, similarity, and augmentation. The proposed approach is named TASH. TASH is a framework based on a spatial index in which tables are mapped and queried. Its goal is to identify the most useful columns for subsequent machine learning tasks. The table retrieval process employs a combination of set containment search and similarity search. Candidate tables are initially identified using set containment search and then ranked based on their similarity to the query. Experimental results demonstrate that TASH can effectively identify joinable tables and select the most relevant features, thereby enabling efficient table augmentation in data lakes. This research contributes to the field of big data by providing a practical solution to the challenges of data integration and analysis in data lake environments

Current trends

Deep parsing is the fundamental process aiming at the representation of the syntactic structure of phrases and sentences. In the traditional methodology this process is based on lexicons and grammars representing roughly properties of words and interactions of words and structures in sentences. Several linguistic frameworks, such as Headdriven Phrase Structure Grammar (HPSG), Lexical Functional Grammar (LFG), Tree Adjoining Grammar (TAG), Combinatory Categorial Grammar (CCG), etc., offer different structures and combining operations for building grammar rules. These already contain mechanisms for expressing properties of Multiword Expressions (MWE), which, however, need improvement in how they account for idiosyncrasies of MWEs on the one hand and their similarities to regular structures on the other hand. This collaborative book constitutes a survey on various attempts at representing and parsing MWEs in the context of linguistic theories and applications

Representation and parsing of multiword expressions

This book consists of contributions related to the definition, representation and parsing of MWEs. These reflect current trends in the representation and processing of MWEs. They cover various categories of MWEs such as verbal, adverbial and nominal MWEs, various linguistic frameworks (e.g. tree-based and unification-based grammars), various languages including English, French, Modern Greek, Hebrew, Norwegian), and various applications (namely MWE detection, parsing, automatic translation) using both symbolic and statistical approaches

Integrated information model for managing the product introduction process

The thesis proposes an integrated product introduction (PI) information model for managing the product introduction process in an efficient manner. Through the process of product introduction, ideas and needs are converted to the information from which technical systems and products can be made and sold. Two critical factors for its success are the management of the product introduction activities, and the quality and functionality of its output (i.e. the product) which itself depends on the quality of the PI process. The process is as effective as the decisions made within it, and as efficient as the speed with which the information required for each decision is made available. In order to improve the efficiency of the management of the project in meeting its diverse targets (project time, project cost, product cost and uparrow product functionality), a model that integrates the targets would be essential in relating the activities of the project to their outcomes. Existing information models in related areas such as design, product development, project management, computer aided design and manufacturing consider some of these targets, but not all of them simultaneously. Especially product functionality is not considered along with the other targets of the PI project. The project introduction information includes managerial and technical information and complex associations among these two categories. Its representation places a challenging and novel set of demands on database technology as it is evolving, distributed and heterogeneous. Existing information models do not address the link between the managerial and technical information, and their continual evolution. Based on a detailed analysis of its nature and content, the thesis presents a three dimensional model of the product introduction information from three related but different viewpoints:- (1) entity-relationship or objects, (2) intra-layer integration and (3) evolution, each capturing important aspects of the PI information, but all required for a complete description. The overall three dimensional information model includes the following layers:- from view 1 - product functionality, process or project, product introduction resources, product and information map; from view 2 - node, relationship, and organisation; from view 3 - meta-model, data model, and data. Each model describes one aspect of the product introduction information but contains references to the others. The building blocks of the information model are described using schema definitions