Publications related to Temperature Control of High Performance Multicore Platforms Using Convex Optimization

Learning-Based Hardware Design for Data Acquisition Systems

This multidisciplinary research work aims to investigate the optimized information extraction from signals or data volumes and to develop tailored hardware implementations that trade-off the complexity of data acquisition with that of data processing, conc ...

EPFL2018

A real-time optimal inverse planning for Gamma Knife radiosurgery by convex optimization: description of the system and first dosimetry data

Jean-Philippe Thiran, Rafael Eduardo Carrillo Rangel, Rémi Charrier

OBJECTIVE The authors developed a new, real-time interactive inverse planning approach, based on a fully convex framework, to be used for Gamma Knife radiosurgery. METHODS The convex framework is based on the precomputation of a dictionary composed of the ...

AMER ASSOC NEUROLOGICAL SURGEONS2018

A Data-Driven Frequency-Domain Approach for Robust Controller Design via Convex Optimization

Achille Nicoletti

The objective of this dissertation is to develop data-driven frequency-domain methods for designing robust controllers through the use of convex optimization algorithms. Many of today's industrial processes are becoming more complex, and modeling accurate ...

EPFL2018

Fast and robust estimation of NODDI parameters using non-Gaussian noise models and spatial regularization

Jean-Philippe Thiran, Erick Jorge Canales Rodriguez, Alessandro Daducci

In this study, we developed a robust inversion algorithm to estimate the Neurite Orientation Dispersion and Density Imaging (NODDI) model. It is based on the Accelerated Microstructure Imaging via Convex Optimization (AMICO) framework. However, in contrast ...

2018

A Data-Driven Approach to Power Converter Control via Convex Optimization

Alireza Karimi, Achille Nicoletti, Michele Martino

A new model reference data-driven approach is presented for synthesizing controllers for the CERN power converter control system. This method uses the frequency re- sponse function (FRF) of a system in order to avoid the problem of unmodeled dynamics assoc ...

2017

Energy-Efficient Near-Threshold Parallel Computing: The PULPv2 Cluster

Andreas Peter Burg, Adam Shmuel Teman, Luca Benini, Jeremy Hugues-Felix Constantin

This article presents an ultra-low-power parallel computing platform and its system-on-chip (SoC) embodiment, targeting a wide range of emerging near-sensor processing tasks for Internet of Things (IoT) applications. The proposed SoC achieves 193 million o ...

Institute of Electrical and Electronics Engineers2017

A Data-Driven Approach in Designing RST Controllers with H∞ Performance via Convex Optimization

Alireza Karimi, Achille Nicoletti, Zlatko Emedi

In this paper, a new method for designing robust fixed-order

\mathcal{H}_{\infty}

discrete-time controllers is presented. The controller structure is a two-degree of freedom polynomial controller of the

RST

type. A data-driven approach is implemented f ...

IEEE2015

Composite convex minimization involving self-concordant-like cost functions

Volkan Cevher, Quoc Tran Dinh, Yen-Huan Li

The self-concordant-like property of a smooth convex func- tion is a new analytical structure that generalizes the self-concordant notion. While a wide variety of important applications feature the self- concordant-like property, this concept has heretofor ...

2015

Near-optimal thermal monitoring framework for many-core systems on chip

Martin Vetterli, David Atienza Alonso, Juri Ranieri, Amina Chebira, Alessandro Vincenzi

Chip designers place on-chip thermal sensors to measure local temperatures, thus preventing thermal runaway situations in many-core processing architectures. However, the quality of the thermal reconstruction is directly dependent on the number of placed s ...

Institute of Electrical and Electronics Engineers2015

Scalable Convex Methods for Phase Retrieval

Volkan Cevher, Alp Yurtsever, Ya-Ping Hsieh

This paper describes scalable convex optimization methods for phase retrieval. The main characteristics of these methods are the cheap per-iteration complexity and the low-memory footprint. With a variant of the original PhaseLift formulation, we first ill ...

2015

Temperature Control of High Performance Multicore Platforms Using Convex Optimization

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Learning-Based Hardware Design for Data Acquisition Systems

A real-time optimal inverse planning for Gamma Knife radiosurgery by convex optimization: description of the system and first dosimetry data

A Data-Driven Frequency-Domain Approach for Robust Controller Design via Convex Optimization

Fast and robust estimation of NODDI parameters using non-Gaussian noise models and spatial regularization

A Data-Driven Approach to Power Converter Control via Convex Optimization

Energy-Efficient Near-Threshold Parallel Computing: The PULPv2 Cluster

A Data-Driven Approach in Designing RST Controllers with H∞ Performance via Convex Optimization

Composite convex minimization involving self-concordant-like cost functions

Near-optimal thermal monitoring framework for many-core systems on chip

Scalable Convex Methods for Phase Retrieval

Learning-Based Hardware Design for Data Acquisition Systems

Fast and robust estimation of NODDI parameters using non-Gaussian noise models and spatial regularization

A real-time optimal inverse planning for Gamma Knife radiosurgery by convex optimization: description of the system and first dosimetry data

Energy-Efficient Near-Threshold Parallel Computing: The PULPv2 Cluster

A Data-Driven Frequency-Domain Approach for Robust Controller Design via Convex Optimization

Near-optimal thermal monitoring framework for many-core systems on chip

A Data-Driven Approach in Designing RST Controllers with H∞ Performance via Convex Optimization

Scalable Convex Methods for Phase Retrieval

A Data-Driven Approach to Power Converter Control via Convex Optimization

Composite convex minimization involving self-concordant-like cost functions