Related papers: SADDLE: A Modular Design Automation Framework for …
Embedding tables are used by machine learning systems to work with categorical features. In modern Recommendation Systems, these tables can be very large, necessitating the development of new methods for fitting them in memory, even during…
In this work, we detail the design and structure of a Synopses Data Engine (SDE) which combines the virtues of parallel processing and stream summarization towards delivering interactive analytics at extreme scale. Our SDE is built on top…
The development of semi-supervised learning (SSL) has in recent years largely focused on the development of new consistency regularization or entropy minimization approaches, often resulting in models with complex training strategies to…
We present an open-source database of superconducting quantum device designs that may be used as the starting point for customized devices. Each design can be generated programmatically using the open-source Qiskit Metal package, and…
Recent years have witnessed a rapid growth of deep-network based services and applications. A practical and critical problem thus has emerged: how to effectively deploy the deep neural network models such that they can be executed…
Modern Hardware Description Languages (HDLs) such as SystemVerilog or VHDL are, due to their sheer complexity, insufficient to transport designs through modern circuit design flows. Instead, each design automation tool lowers HDLs to its…
As distributed systems grow in scale and complexity, the need for flexible automation of systems management functions also grows. We outline a framework for building tools that provide distributed, scalable, declarative, modular, and…
The paper addresses advancements in Generative Artificial Intelligence (GenAI) and digital chip design, highlighting the integration of Large Language Models (LLMs) in automating hardware description and design. LLMs, known for generating…
We introduce SysML-Sec, a SysML-based Model-Driven Engineering environment aimed at fostering the collaboration between system designers and security experts at all methodological stages of the development of an embedded system. A central…
Semi-supervised learning (SSL) aims to help reduce the cost of the manual labelling process by leveraging a substantial pool of unlabelled data alongside a limited set of labelled data during the training phase. Since pixel-level manual…
Given the potential difficulties in obtaining large quantities of labelled data, many works have explored the use of deep semi-supervised learning, which uses both labelled and unlabelled data to train a neural network architecture. The…
Dirichlet process mixture (DPM) models are widely used for semiparametric Bayesian analysis in educational and behavioral research, yet specifying the concentration parameter remains a critical barrier. Default hyperpriors often impose…
Designing controllers for complex industrial electronic systems is challenging due to nonlinearities and parameter uncertainties, and traditional methods are often slow and costly. To address this, we propose a novel autonomous design…
Tools are an essential part of every software engineering project. But the number of tools that are used in all phases of the software development life-cycle and their complexity is growing continually. Consequently, the setup and…
This work introduces TopoBench, an open-source library designed to standardize benchmarking and accelerate research in topological deep learning (TDL). TopoBench decomposes TDL into a sequence of independent modules for data generation,…
Support vector clustering (SVC) is a versatile clustering technique that is able to identify clusters of arbitrary shapes by exploiting the kernel trick. However, one hurdle that restricts the application of SVC lies in its sensitivity to…
Swarms of autonomous devices are increasing in ubiquity and size, making the need for rethinking their hardware-software system stack critical. We present HiveMind, the first swarm coordination platform that enables programmable execution…
The advent of software defined networking enables flexible, reliable and feature-rich control planes for data center networks. However, the tight coupling of centralized control and complete visibility leads to a wide range of issues among…
We present the Toolbox for Analysis and Large-scale Optimization of Spacecraft (TALOS), a framework designed for applying large-scale multidisciplinary design optimization (MDO) to spacecraft design problems. The framework is built using…
As tools for designing multiple processor systems-on-chips (MPSoCs) continue to evolve to meet the demands of developers, there exist systematic gaps that must be bridged to provide a more cohesive hardware/software development environment.…