Distributed Computing Laboratory

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The scale and pervasiveness of the Internet make it a pillar of planetary communication, industry and economy, as well as a fundamental medium for public discourse and democratic engagement. In stark contrast with the Internet's decentralized infrastructur ...

Computer systems rely heavily on abstraction to manage the exponential growth of complexity across hardware and software. Due to practical considerations of compatibility between components of these complex systems across generations, developers have favou ...

In light of the challenges posed by climate change and the goals of the Paris Agreement, electricity generation is shifting to a more renewable and decentralized pattern, while the operation of systems like buildings is increasingly electrified. This calls ...

Distributed learning is the key for enabling training of modern large-scale machine learning models, through parallelising the learning process. Collaborative learning is essential for learning from privacy-sensitive data that is distributed across various ...

Decision-making permeates every aspect of human and societal development, from individuals' daily choices to the complex decisions made by communities and institutions. Central to effective decision-making is the discipline of optimization, which seeks th ...

Enterprises collect data in large volumes and leverage them to drive numerous concurrent decisions and business processes. Their teams deploy multiple applications that often operate concurrently on the same data and infrastructure but have widely differen ...

In this paper, we propose an analytical stochastic dynamic programming (SDP) algorithm to address the optimal management problem of price-maker community energy storage. As a price-maker, energy storage smooths price differences, thus decreasing energy arb ...

Driven by the demand for real-time processing and the need to minimize latency in AI algorithms, edge computing has experienced remarkable progress. Decision-making AI applications stand out for their heavy reliance on data-centric operations, predominantl ...

The desire and ability to place AI-enabled applications on the edge has grown significantly in recent years. However, the compute-, area-, and power-constrained nature of edge devices are stressed by the needs of the AI-enabled applications, due to a gener ...

Verification and testing of hardware heavily relies on cycle-accurate simulation of RTL. As single-processor performance is growing only slowly, conventional, single-threaded RTL simulation is becoming impractical for increasingly complex chip designs and ...

This paper offers a new algorithm to efficiently optimize scheduling decisions for dial-a-ride problems (DARPs), including problem variants considering electric and autonomous vehicles (e-ADARPs). The scheduling heuristic, based on linear programming theor ...

The field of biometrics, and especially face recognition, has seen a wide-spread adoption the last few years, from access control on personal devices such as phones and laptops, to automated border controls such as in airports. The stakes are increasingly ...

Machine learning algorithms such as Convolutional Neural Networks (CNNs) are characterized by high robustness towards quantization, supporting small-bitwidth fixed-point arithmetic at inference time with little to no degradation in accuracy. In turn, small ...

Modern integrated circuits are tiny yet incredibly complex technological artifacts, composed of millions and billions of individual structures working in unison. Managing their complexity and facilitating their design drove part of the co-evolution of mode ...

Data races have long been a notorious problem in concurrent programming. They are subtle to detect, and lead to non-deterministic behaviours. There has been a lot of interest in type systems that statically guarantee data race freedom. Significant progress ...

In the rapidly evolving landscape of machine learning research, neural networks stand out with their ever-expanding number of parameters and reliance on increasingly large datasets. The financial cost and computational resources required for the training p ...

How to measure students' Computational Problem-Solving (CPS) competencies is an ongoing research topic. Prevalent approaches vary by measurement tools (e.g., interactive programming, multiple-choice tests, or programming-independent tests) and task types ( ...

The rapid development of wearable biomedical systems now enables real-time monitoring of electroencephalography (EEG) signals. Acquisition of these signals relies on electrodes. These systems must meet the design challenge of selecting an optimal set of el ...

Modern neuroscience research is generating increasingly large datasets, from recording thousands of neurons over long timescales to behavioral recordings of animals spanning weeks, months, or even years. Despite a great variety in recording setups and expe ...

We introduce an algorithm to reconstruct a mesh from discrete samples of a shape's Signed Distance Function (SDF). A simple geometric reinterpretation of the SDF lets us formulate the problem through a point cloud, from which a surface can be extracted wit ...

Distributed Computing Laboratory

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Planetary-Scale Byzantine Fault Tolerance

Secure Interface Design Leveraging Hardware/Software Support

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

Reliable data-driven decision-making through optimal transport

Oligolithic Cross-task Optimizations across Isolated Workloads

Energy Management of Price-Maker Community Energy Storage by Stochastic Dynamic Programming

EdgeAI-Aware Design of In-Memory Computing Architectures

Exploring High-Performance and Energy-Efficient Architectures for Edge AI-Enabled Applications

Highly Parallel RTL Simulation

A ride time-oriented scheduling algorithm for dial-a-ride problems

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

DBFS: Dynamic Bitwidth-Frequency Scaling for Efficient Software-defined SIMD

Graph generative deep learning models with an application to circuit topologies

Degrees of Separation: A Flexible Type System for Safe Concurrency

Random matrix methods for high-dimensional machine learning models

A Comparative Analysis of Tools & Task Types for Measuring Computational Problem-Solving

FETCH: A Fast and Efficient Technique for Channel Selection in EEG Wearable Systems

Robust machine learning for neuroscientific inference

Reach For the Arcs: Reconstructing Surfaces from SDFs via Tangent Points