Compressed Sensing for Real-Time Energy-Efficient ECG Compression on Wireless Body Sensor Nodes

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Publications associées (29)

Fast Energy Estimation Through Partial Execution of HPC Applications

Marina Zapater Sancho, José Luis Ayala Rodrigo

In order to optimize the energy use of servers in Data Centers, techniques such as power capping or power budgeting are usually deployed. These techniques rely on the prediction of the power and execution time of applications. These data are obtained via d ...

IEEE2018

Interleaving with coroutines: a systematic and practical approach to hide memory latency in index joins

Anastasia Ailamaki, Georgios Psaropoulos

Index joins present a case of pointer-chasing code that causes data cache misses. In principle, we can hide these cache misses by overlapping them with computation: The lookups involved in an index join are parallel tasks whose execution can be interleaved ...

2019

Integrating Heuristic and Machine-Learning Methods for Efficient Virtual Machine Allocation in Data Centers

David Atienza Alonso, Marina Zapater Sancho, Ali Pahlevan, Xiaoyu Qu

Modern cloud data centers (DCs) need to tackle efficiently the increasing demand for computing resources and address the energy efficiency challenge. Therefore, it is essential to develop resource provisioning policies that are aware of virtual machine (VM ...

2018

Exploiting Compute Caches for Memory Bound Vector Operations

Paolo Ienne, Gabriel Falcao Paiva Fernandes, João Miguel Morgado Pereira Vieira

To reduce the average memory access time, most current processors make use of a multilevel cache subsystem. However, despite the proven benefits of such cache structures in the resulting throughput, conventional operations such as copy, simple maps and red ...

IEEE2018

3D Bpconvnet To Reconstruct Parallel Mri

Michaël Unser, Kyong Hwan Jin

In recent years, compressed sensing techniques have been applied to the reconstruction of parallel magnetic resonance (MR) images. Particularly for 3D MR signal, it is crucial to acquire fewer samples to reduce the distortions caused by long-time acquisiti ...

IEEE2018

Adaptive Cache Mode Selection for Queries over Raw Data

Anastasia Ailamaki, Tahir Azim, Azqa Nadeem

Caching the results of intermediate query results for future re-use is a common technique for improving the performance of analytics over raw data sources. An important design choice in this regard is whether to lazily cache only the offsets of satisfying ...

2018

Automated Synthesis of Adversarial Workloads for Network Functions

Rishabh Ramesh Iyer, Aikaterini Argyraki, Arseniy Zaostrovnykh, Jonas Fietz, Luis David Figueiredo Mascarenhas Moreira Pedrosa

Software network functions promise to simplify the deployment of network services and reduce network operation cost. However, they face the challenge of unpredictable performance. Given this performance variability, it is imperative that during deployment, ...

ACM2018

Trading off Energy versus Accuracy in Modern Computing Systems : From Digital Circuit Design to Programming Techniques

Jérémy Lucien Maurice Schlachter

The slowdown of Moore's law, which has been the driving force of the electronics industry over the last 5 decades, is causing serious problem to Integrated Circuits (ICs) improvements. Technology scaling is becoming more and more complex and fabrication co ...

EPFL2018

The Mondrian Data Engine

Babak Falsafi, Boris Robert Grot, Alexandros Daglis, Javier Picorel Obando, Mario Paulo Drumond Lages De Oliveira, Dmitrii Ustiugov, Dionysios Pnevmatikatos, Nooshin Sadat Mirzadeh

The increasing demand for extracting value out of ever-growing data poses an ongoing challenge to system designers, a task only made trickier by the end of Dennard scaling. As the performance density of traditional CPU-centric architectures stagnates, adva ...

2017

An Adaptive Sublinear-Time Block Sparse Fourier Transform

Volkan Cevher, Mikhail Kapralov, Jonathan Mark Scarlett, Amir Zandieh

The problem of approximately computing the

k

dominant Fourier coefficients of a vector

X

quickly, and using few samples in time domain, is known as the Sparse Fourier Transform (sparse FFT) problem. A long line of work on the sparse FFT has resulted in ...

2017

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k

dominant Fourier coefficients of a vector

X

quickly, and using few samples in time domain, is known as the Sparse Fourier Transform (sparse FFT) problem. A long line of work on the sparse FFT has resulted in ...

2017