A Flexible In-Memory Computing Architecture for Heterogeneously Quantized CNNs

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The ability of the brain to produce adaptive and dexterous movements relies on the seamless integration of sensory feedback into motor commands. Despite significant advances in motor neuroscience, critical gaps persist in understanding how proprioceptive a ...

Computational methods at the atomic scale based on machine learning have been a fruitful research field in the past decade by merging the advantages of classical and quantum mechanical approaches. While parts of the field are already reaching a mature stat ...

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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 ...

Two design studios were successively taught in two schools of architecture: one at the Harvard Graduate School of Design (GSD) and the other at the Swiss Federal Institute of Technology in Lausanne (EPFL) in Lausanne, Switzerland, based on "A Global Morato ...

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Frontal polymerisation has the potential to bring unprecedented reductions in energy demand and process time to produce fibre reinforced polymer composites. Production of epoxy-based fibre reinforced polymer parts with high fibre volume content, commonly e ...

Background: The interpretation of lung sounds plays a crucial role in the appropriate diagnosis and management of pediatric asthma. Applying artificial intelligence (AI) to this task has the potential to better standardize assessment and may even improve i ...

Building climate risk assessment involves benchmarking a building's energy use intensity against decarbonisation pathways to mitigate the impacts on climate change. Various climate risk assessment tools and frameworks are used for commercial buildings in d ...

In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

2

-layer diagonal linear networks. This rudimentary architecture, which consists of a two layer feedforward linear network with a diagonal ...

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The global construction industry contributes to 37% of carbon emissions associated to both building operations and construction. To help achieve the net-zero targets set by 2050, it is mandated to achieve a 50% reduction in carbon emissions by 2030. As we ...

We present a finite elements-neural network approach for the numerical approximation of parametric partial differential equations. The algorithm generates training data from finite element simulations, and uses a data -driven (supervised) feedforward neura ...

A Flexible In-Memory Computing Architecture for Heterogeneously Quantized CNNs

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Reverse engineering primate sensorimotor control with machine learning

Mathematical Analysis and Optimization of Features for Atomistic Machine Learning

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

In-Sensor Passive Speech Classification with Phononic Metamaterials

Solar-assisted clean steam generator, a cleaner production approach for sustainable healthcare facilities

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

Teaching Not To

Electrical and Optical Manifestations of Flat Band Physics in Van der Waals Materials

Explainable Fault Diagnosis of Oil-Immersed Transformers: A Glass-Box Model

EdgeAI-Aware Design of In-Memory Computing Architectures

Co-Design Optimisation of Morphing Topology and Control of Winged Drones

Geometry-Driven Stock-Constrained Truss Design via Equilibrium-Based Structural Models

Self-catalysed frontal polymerisation enables fast and low-energy processing of fibre reinforced polymer composites

Automated Interpretation of Lung Sounds by Deep Learning in Children With Asthma: Scoping Review and Strengths, Weaknesses, Opportunities, and Threats Analysis

Climate risk assessment of buildings: An analysis of operating emissions of commercial offices in Australia

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Robust NAS under adversarial training: benchmark, theory, and beyond

Random matrix methods for high-dimensional machine learning models

An explorative method to support design decisions based on carbon constraints and daylight sufficiency needs

Error assessment of an adaptive finite elements-neural networks method for an elliptic parametric PDE