USSR: An UltraSound Sparse Regularization framework

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Pulse-Echo Ultrasound Imaging suffers from several sources of image degradation. In clinical conditions, superficial layers made of different tissues (e.g. skin, fat or muscles) create aberrations that can severely deteriorate image quality. To correct suc ...

The remarkable ability of deep learning (DL) models to approximate high-dimensional functions from samples has sparked a revolution across numerous scientific and industrial domains that cannot be overemphasized. In sensitive applications, the good perform ...

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Embedded systems confront two opposite goals: low-power operation and high performance. The current trend to reach these goals is toward heterogeneous platforms, including multi-core architectures with heterogeneous cores and hardware accelerators. The lat ...

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The functional organization of neural processes is constrained by the brain's intrinsic structural connectivity, i.e., the connectome. Here, we explore how structural connectivity can improve the representation of brain activity signals and their dynamics. ...

Among the medical imaging modalities, ultrasound (US) imaging is one of the safest, most widespread, and least expensive method used in medical diagnosis. In the past decades, several technological advances enabled the advent of ultrafast US imaging, an ac ...

Thanks to its capability of acquiring full-view frames at multiple kilohertz, ultrafast ultrasound imaging unlocked the analysis of rapidly changing physical phenomena in the human body, with pioneering applications such as ultrasensitive flow imaging in t ...

Time series with missing data are signals encountered in important settings for machine learning. Some of the most successful prior approaches for modeling such time series are based on recurrent neural networks that transform the input and previous state ...

Gyrokinetic codes in plasma physics need outstanding computational resources to solve increasingly complex problems, requiring the effective exploitation of cutting-edge HPC architectures. This paper focuses on the enabling of ORB5, a state-of-the-art, fir ...

RePlAce is a state-of-the-art prototype of a flat, analytic, and nonlinear global cell placement algorithm, which models a placement instance as an electrostatic system with positively charged objects. It can handle large-scale standard-cell and mixed-cell ...

In-memory computing is an emerging non-von Neumann computing paradigm where certain computational tasks are performed in memory by exploiting the physical attributes of the memory devices. Memristive devices such as phase-change memory (PCM), where informa ...

FPGAs (Field Programmable Gate Array) are an attractive technology for high-speed data processing in space missions due to their unbeatable flexibility and best performance-to-power ratio in comparison to software. However FPGAs suffer from 3 major drawbac ...

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In a drive to maximize resource utilization, today's datacenters are moving to colocation of latency-sensitive and batch workloads on the same server. State-of-the-art deployments, such as those at Google, colocate such diverse workloads even on a single S ...

Conventional ultrasound (US) image reconstruction methods rely on delay-and-sum (DAS) beamforming, which is a relatively poor solution of the image reconstruction problem. An alternative to DAS consists in using iterative techniques which require both an a ...

Spectre, Meltdown, and related attacks have demonstrated that kernels, hypervisors, trusted execution environments, and browsers are prone to information disclosure through micro-architectural weaknesses. However, it remains unclear as to what extent other ...

We present an application of massively parallel processing of quantitative flow measurements data acquired using spectral optical coherence microscopy (SOCM). The need for massive signal processing of these particular datasets has been a major hurdle for m ...

Database systems access memory either sequentially or randomly. Contrary to sequential access and despite the extensive efforts of computer architects, compiler writers, and system builders, random access to data larger than the processor cache has been s ...

USSR: An UltraSound Sparse Regularization framework

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A Path-Based Model for Aberration Correction in Ultrasound Imaging

Towards Trustworthy Deep Learning for Image Reconstruction

Exploring brain-inspired multi-core heterogeneous hardware templates for low-power biomedical embedded systems

Acceleration of Control Intensive Applications on Coarse-Grained Reconfigurable Arrays for Embedded Systems

Self-Attentive Spatial Adaptive Normalization for Cross-Modality Domain Adaptation

Architecture Exploration and Optimization of Heterogeneous Many-Core Compute and Memory Architectures with Architectural Extensions

The connectome spectrum as a canonical basis for a sparse representation of fast brain activity

Ultrasound Imaging: From Physical Modeling to Deep Learning

CNN-Based Ultrasound Image Reconstruction for Ultrafast Displacement Tracking

Recursive Input And State Estimation: A General Framework For Learning From Time Series With Missing Data

Gyrokinetic simulations on many- and multi-core architectures with the global electromagnetic Particle-In-Cell Code ORB5

A Shared-Memory Parallel Implementation of the RePlAce Global Cell Placer

Accurate Emulation of Memristive Crossbar Arrays for In-Memory Computing

A Dynamically Reconfigurable Platform for High-Performance and Low-Power On-Board Processing

Computational Aesthetics and Image Enhancements using Deep Neural Networks

Stretch: Balancing QoS and Throughput for Colocated Server Workloads on SMT Cores

Ultrafast ultrasound imaging as an inverse problem: Matrix-free sparse image reconstruction

SMoTherSpectre: Exploiting Speculative Execution through Port Contention

Massively parallel data processing for quantitative total flow imaging with optical coherence microscopy and tomography

Improving Main-memory Database System Performance through Cooperative Multitasking