Project acronym COHOMCODES
Project Robust Codes from Higher Dimesional Expanders
Researcher (PI) Tali Kaufman Halman
Host Institution (HI) BAR ILAN UNIVERSITY
Call Details Starting Grant (StG), PE6, ERC-2013-StG
Summary Error correcting codes play a fundamental role in computer science. Good codes are codes with rate and distance that are asymptotically optimal. Some of the most successful good codes are constructed using expander graphs. In recent years a new notion of {\em robust} error correcting codes, known as locally testable codes (LTCs), has emerged. Locally testable codes are codes in which a proximity of a vector to an error correcting code can be achieved by probing the vector in {\em constant} many locations (independent of its length). LTCs are at the heart of Probabilistically Checkable Proofs (PCPs) and their construction has been sought since the discovery of the PCP theorem in the early 1990s.
Despite 20 years of research, it is still widely open whether good locally testable codes exist. LTCs present completely new challenge to the field of error correcting codes. In the old paradigm a random code is a good code and the main focus was to construct explicit codes that imitate the random code. However, a random code is not an LTC. Thus, contrary to traditional codes, there are no natural candidates for LTCs. The known constructions of robust codes are ad hoc, and there is a lack of theory that explains their existence.
The goal of the current research plan is to harness the emerging field of higher dimensional expanders and their topological properties for a systematic study of robust error correcting codes. Higher dimensional expanders are natural candidates for obtaining robust codes since they offer a strong form of redundancy that is essential for robustness. Such form of redundancy is lacking by their one dimensional analogue (i.e., expander graphs). Hence, the known expander codes are not robust. We expect that our study will draw new connections between error correcting codes, high dimensional expanders, topology and probability that will shed new light on these fields, and in particular, will advance the constructing of good and robust codes.
Summary
Error correcting codes play a fundamental role in computer science. Good codes are codes with rate and distance that are asymptotically optimal. Some of the most successful good codes are constructed using expander graphs. In recent years a new notion of {\em robust} error correcting codes, known as locally testable codes (LTCs), has emerged. Locally testable codes are codes in which a proximity of a vector to an error correcting code can be achieved by probing the vector in {\em constant} many locations (independent of its length). LTCs are at the heart of Probabilistically Checkable Proofs (PCPs) and their construction has been sought since the discovery of the PCP theorem in the early 1990s.
Despite 20 years of research, it is still widely open whether good locally testable codes exist. LTCs present completely new challenge to the field of error correcting codes. In the old paradigm a random code is a good code and the main focus was to construct explicit codes that imitate the random code. However, a random code is not an LTC. Thus, contrary to traditional codes, there are no natural candidates for LTCs. The known constructions of robust codes are ad hoc, and there is a lack of theory that explains their existence.
The goal of the current research plan is to harness the emerging field of higher dimensional expanders and their topological properties for a systematic study of robust error correcting codes. Higher dimensional expanders are natural candidates for obtaining robust codes since they offer a strong form of redundancy that is essential for robustness. Such form of redundancy is lacking by their one dimensional analogue (i.e., expander graphs). Hence, the known expander codes are not robust. We expect that our study will draw new connections between error correcting codes, high dimensional expanders, topology and probability that will shed new light on these fields, and in particular, will advance the constructing of good and robust codes.
Max ERC Funding
1 302 000 €
Duration
Start date: 2014-02-01, End date: 2020-01-31
Project acronym COLORAMAP
Project Constrained Low-Rank Matrix Approximations: Theoretical and Algorithmic Developments for Practitioners
Researcher (PI) Nicolas Benoit P Gillis
Host Institution (HI) UNIVERSITE DE MONS
Call Details Starting Grant (StG), PE6, ERC-2015-STG
Summary Low-rank matrix approximation (LRA) techniques such as principal component analysis (PCA) are powerful tools for the representation and analysis of high dimensional data, and are used in a wide variety of areas such as machine learning, signal and image processing, data mining, and optimization. Without any constraints and using the least squares error, LRA can be solved via the singular value decomposition. However, in practice, this model is often not suitable mainly because (i) the data might be contaminated with outliers, missing data and non-Gaussian noise, and (ii) the low-rank factors of the decomposition might have to satisfy some specific constraints. Hence, in recent years, many variants of LRA have been introduced, using different constraints on the factors and using different objective functions to assess the quality of the approximation; e.g., sparse PCA, PCA with missing data, independent component analysis and nonnegative matrix factorization. Although these new constrained LRA models have become very popular and standard in some fields, there is still a significant gap between theory and practice. In this project, our goal is to reduce this gap by attacking the problem in an integrated way making connections between LRA variants, and by using four very different but complementary perspectives: (1) computational complexity issues, (2) provably correct algorithms, (3) heuristics for difficult instances, and (4) application-oriented aspects. This unified and multi-disciplinary approach will enable us to understand these problems better, to develop and analyze new and existing algorithms and to then use them for applications. Our ultimate goal is to provide practitioners with new tools and to allow them to decide which method to use in which situation and to know what to expect from it.
Summary
Low-rank matrix approximation (LRA) techniques such as principal component analysis (PCA) are powerful tools for the representation and analysis of high dimensional data, and are used in a wide variety of areas such as machine learning, signal and image processing, data mining, and optimization. Without any constraints and using the least squares error, LRA can be solved via the singular value decomposition. However, in practice, this model is often not suitable mainly because (i) the data might be contaminated with outliers, missing data and non-Gaussian noise, and (ii) the low-rank factors of the decomposition might have to satisfy some specific constraints. Hence, in recent years, many variants of LRA have been introduced, using different constraints on the factors and using different objective functions to assess the quality of the approximation; e.g., sparse PCA, PCA with missing data, independent component analysis and nonnegative matrix factorization. Although these new constrained LRA models have become very popular and standard in some fields, there is still a significant gap between theory and practice. In this project, our goal is to reduce this gap by attacking the problem in an integrated way making connections between LRA variants, and by using four very different but complementary perspectives: (1) computational complexity issues, (2) provably correct algorithms, (3) heuristics for difficult instances, and (4) application-oriented aspects. This unified and multi-disciplinary approach will enable us to understand these problems better, to develop and analyze new and existing algorithms and to then use them for applications. Our ultimate goal is to provide practitioners with new tools and to allow them to decide which method to use in which situation and to know what to expect from it.
Max ERC Funding
1 291 750 €
Duration
Start date: 2016-09-01, End date: 2021-08-31
Project acronym CombiCompGeom
Project Combinatorial Aspects of Computational Geometry
Researcher (PI) Natan Rubin
Host Institution (HI) BEN-GURION UNIVERSITY OF THE NEGEV
Call Details Starting Grant (StG), PE6, ERC-2015-STG
Summary The project focuses on the interface between computational and combinatorial geometry.
Geometric problems emerge in a variety of computational fields that interact with the physical world.
The performance of geometric algorithms is determined by the description complexity of their underlying combinatorial structures. Hence, most theoretical challenges faced by computational geometry are of a distinctly combinatorial nature.
In the past two decades, computational geometry has been revolutionized by the powerful combination of random sampling techniques with the abstract machinery of geometric arrangements. These insights were used, in turn, to establish state-of-the-art results in combinatorial geometry. Nevertheless, a number of fundamental problems remained open and resisted numerous attempts to solve them.
Motivated by the recent breakthrough results, in which the PI played a central role, we propose two exciting lines of study with the potential to change the landscape of this field.
The first research direction concerns the complexity of Voronoi diagrams -- arguably the most common structures in computational geometry.
The second direction concerns combinatorial and algorithmic aspects of geometric intersection structures, including some fundamental open problems in geometric transversal theory. Many of these questions are motivated by geometric variants of general covering and packing problems, and all efficient approximation schemes for them must rely on the intrinsic properties of geometric graphs and hypergraphs.
Any progress in responding to these challenges will constitute a major breakthrough in both computational and combinatorial geometry.
Summary
The project focuses on the interface between computational and combinatorial geometry.
Geometric problems emerge in a variety of computational fields that interact with the physical world.
The performance of geometric algorithms is determined by the description complexity of their underlying combinatorial structures. Hence, most theoretical challenges faced by computational geometry are of a distinctly combinatorial nature.
In the past two decades, computational geometry has been revolutionized by the powerful combination of random sampling techniques with the abstract machinery of geometric arrangements. These insights were used, in turn, to establish state-of-the-art results in combinatorial geometry. Nevertheless, a number of fundamental problems remained open and resisted numerous attempts to solve them.
Motivated by the recent breakthrough results, in which the PI played a central role, we propose two exciting lines of study with the potential to change the landscape of this field.
The first research direction concerns the complexity of Voronoi diagrams -- arguably the most common structures in computational geometry.
The second direction concerns combinatorial and algorithmic aspects of geometric intersection structures, including some fundamental open problems in geometric transversal theory. Many of these questions are motivated by geometric variants of general covering and packing problems, and all efficient approximation schemes for them must rely on the intrinsic properties of geometric graphs and hypergraphs.
Any progress in responding to these challenges will constitute a major breakthrough in both computational and combinatorial geometry.
Max ERC Funding
1 303 750 €
Duration
Start date: 2016-09-01, End date: 2021-08-31
Project acronym COMET
Project foundations of COmputational similarity geoMETtry
Researcher (PI) Michael Bronstein
Host Institution (HI) UNIVERSITA DELLA SVIZZERA ITALIANA
Call Details Starting Grant (StG), PE6, ERC-2012-StG_20111012
Summary "Similarity is one of the most fundamental notions encountered in problems practically in every branch of science, and is especially crucial in image sciences such as computer vision and pattern recognition. The need to quantify similarity or dissimilarity of some data is central to broad categories of problems involving comparison, search, matching, alignment, or reconstruction. The most common way to model a similarity is using metrics (distances). Such constructions are well-studied in the field of metric geometry, and there exist numerous computational algorithms allowing, for example, to represent one metric using another by means of isometric embeddings.
However, in many applications such a model appears to be too restrictive: many types of similarity are non-metric; it is not always possible to model the similarity precisely or completely e.g. due to missing data; some objects might be mutually incomparable e.g. if they are coming from different modalities. Such deficiencies of the metric similarity model are especially pronounced in large-scale computer vision, pattern recognition, and medical imaging applications.
The ambitious goal of this project is to introduce a paradigm shift in the way we model and compute similarity. We will develop a unifying framework of computational similarity geometry that extends the theoretical metric model, and will allow developing efficient numerical and computational tools for the representation and computation of generic similarity models. The methods will be developed all the way from mathematical concepts to efficiently implemented code and will be applied to today’s most important and challenging problems in Internet-scale computer vision and pattern recognition, shape analysis, and medical imaging."
Summary
"Similarity is one of the most fundamental notions encountered in problems practically in every branch of science, and is especially crucial in image sciences such as computer vision and pattern recognition. The need to quantify similarity or dissimilarity of some data is central to broad categories of problems involving comparison, search, matching, alignment, or reconstruction. The most common way to model a similarity is using metrics (distances). Such constructions are well-studied in the field of metric geometry, and there exist numerous computational algorithms allowing, for example, to represent one metric using another by means of isometric embeddings.
However, in many applications such a model appears to be too restrictive: many types of similarity are non-metric; it is not always possible to model the similarity precisely or completely e.g. due to missing data; some objects might be mutually incomparable e.g. if they are coming from different modalities. Such deficiencies of the metric similarity model are especially pronounced in large-scale computer vision, pattern recognition, and medical imaging applications.
The ambitious goal of this project is to introduce a paradigm shift in the way we model and compute similarity. We will develop a unifying framework of computational similarity geometry that extends the theoretical metric model, and will allow developing efficient numerical and computational tools for the representation and computation of generic similarity models. The methods will be developed all the way from mathematical concepts to efficiently implemented code and will be applied to today’s most important and challenging problems in Internet-scale computer vision and pattern recognition, shape analysis, and medical imaging."
Max ERC Funding
1 495 020 €
Duration
Start date: 2012-10-01, End date: 2017-09-30
Project acronym COMPCAMERAANALYZ
Project Understanding Designing and Analyzing Computational Cameras
Researcher (PI) Anat Levin
Host Institution (HI) WEIZMANN INSTITUTE OF SCIENCE LTD
Call Details Starting Grant (StG), PE6, ERC-2010-StG_20091028
Summary Computational cameras go beyond 2D images and allow the extraction of more dimensions from the visual world such as depth, multiple viewpoints and multiple illumination conditions. They also allow us to overcome some of the traditional photography challenges such as defocus blur, motion blur, noise and resolution. The increasing variety of computational cameras is raising the need for a meaningful comparison across camera types. We would like to understand which cameras are better for specific tasks, which aspects of a camera make it better than others and what is the best performance we can hope to achieve.
Our 2008 paper introduced a general framework to address the design and analysis of computational cameras. A camera is modeled as a linear projection in ray space. Decoding the camera data then deals with inverting the linear projection. Since the number of sensor measurements is usually much smaller than the number of rays, the inversion must be treated as a Bayesian inference problem accounting for prior knowledge on the world.
Despite significant progress which has been made in the recent years, the space of computational cameras is still far from being understood.
Computational camera analysis raises the following research challenges: 1) What is a good way to model prior knowledge on ray space? 2) Seeking efficient inference algorithms and robust ways to decode the world from the camera measurements. 3) Evaluating the expected reconstruction accuracy of a given camera. 4) Using the expected reconstruction performance for evaluating and comparing camera types. 5) What is the best camera? Can we derive upper bounds on the optimal performance?
We propose research on all aspects of computational camera design and analysis. We propose new prior models which will significantly simplify the inference and evaluation tasks. We also propose new ways to bound and evaluate computational cameras with existing priors.
Summary
Computational cameras go beyond 2D images and allow the extraction of more dimensions from the visual world such as depth, multiple viewpoints and multiple illumination conditions. They also allow us to overcome some of the traditional photography challenges such as defocus blur, motion blur, noise and resolution. The increasing variety of computational cameras is raising the need for a meaningful comparison across camera types. We would like to understand which cameras are better for specific tasks, which aspects of a camera make it better than others and what is the best performance we can hope to achieve.
Our 2008 paper introduced a general framework to address the design and analysis of computational cameras. A camera is modeled as a linear projection in ray space. Decoding the camera data then deals with inverting the linear projection. Since the number of sensor measurements is usually much smaller than the number of rays, the inversion must be treated as a Bayesian inference problem accounting for prior knowledge on the world.
Despite significant progress which has been made in the recent years, the space of computational cameras is still far from being understood.
Computational camera analysis raises the following research challenges: 1) What is a good way to model prior knowledge on ray space? 2) Seeking efficient inference algorithms and robust ways to decode the world from the camera measurements. 3) Evaluating the expected reconstruction accuracy of a given camera. 4) Using the expected reconstruction performance for evaluating and comparing camera types. 5) What is the best camera? Can we derive upper bounds on the optimal performance?
We propose research on all aspects of computational camera design and analysis. We propose new prior models which will significantly simplify the inference and evaluation tasks. We also propose new ways to bound and evaluate computational cameras with existing priors.
Max ERC Funding
756 845 €
Duration
Start date: 2010-12-01, End date: 2015-11-30
Project acronym CompDB
Project The Computational Database for Real World Awareness
Researcher (PI) Thomas NEUMANN
Host Institution (HI) TECHNISCHE UNIVERSITAET MUENCHEN
Call Details Consolidator Grant (CoG), PE6, ERC-2016-COG
Summary Two major hardware trends have a significant impact on the architecture of database management systems (DBMSs): First, main memory sizes continue to grow significantly. Machines with 1TB of main memory and more are readily available at a relatively low price. Second, the number of cores in a system continues to grow, from currently 64 and more to hundreds in the near future.
This trend offers radically new opportunities for both business and science. It promises to allow for information-at-your-fingertips, i.e., large volumes of data can be analyzed and deeply explored online, in parallel to regular transaction processing. Currently, deep data exploration is performed outside of the database system which necessitates huge data transfers. This impedes the processing such that real-time interactive exploration is impossible. These new hardware capabilities now allow to build a true computational database system that integrates deep exploration functionality at the source of the data. This will lead to a drastic shift in how users interact with data, as for the first time interactive data exploration becomes possible at a massive scale.
Unfortunately, traditional DBMSs are simply not capable to tackle these new challenges.
Traditional techniques like interpreted code execution for query processing become a severe bottleneck in the presence of such massive parallelism, causing poor utilization of the hardware. I pursue a radically different approach: Instead of adapting the traditional, disk-based approaches, I am integrating a new just-in-time compilation framework into the in-memory database that directly exploits the abundant, parallel hardware for large-scale data processing and exploration. By explicitly utilizing cores, I will be able to build a powerful computational database engine that scales the entire spectrum of data processing - from transactional to analytical to exploration workflows - far beyond traditional architectures.
Summary
Two major hardware trends have a significant impact on the architecture of database management systems (DBMSs): First, main memory sizes continue to grow significantly. Machines with 1TB of main memory and more are readily available at a relatively low price. Second, the number of cores in a system continues to grow, from currently 64 and more to hundreds in the near future.
This trend offers radically new opportunities for both business and science. It promises to allow for information-at-your-fingertips, i.e., large volumes of data can be analyzed and deeply explored online, in parallel to regular transaction processing. Currently, deep data exploration is performed outside of the database system which necessitates huge data transfers. This impedes the processing such that real-time interactive exploration is impossible. These new hardware capabilities now allow to build a true computational database system that integrates deep exploration functionality at the source of the data. This will lead to a drastic shift in how users interact with data, as for the first time interactive data exploration becomes possible at a massive scale.
Unfortunately, traditional DBMSs are simply not capable to tackle these new challenges.
Traditional techniques like interpreted code execution for query processing become a severe bottleneck in the presence of such massive parallelism, causing poor utilization of the hardware. I pursue a radically different approach: Instead of adapting the traditional, disk-based approaches, I am integrating a new just-in-time compilation framework into the in-memory database that directly exploits the abundant, parallel hardware for large-scale data processing and exploration. By explicitly utilizing cores, I will be able to build a powerful computational database engine that scales the entire spectrum of data processing - from transactional to analytical to exploration workflows - far beyond traditional architectures.
Max ERC Funding
1 918 750 €
Duration
Start date: 2017-06-01, End date: 2022-05-31
Project acronym COMPECON
Project Complexity and Simplicity in Economic Mechanisms
Researcher (PI) Noam NISAN
Host Institution (HI) THE HEBREW UNIVERSITY OF JERUSALEM
Call Details Advanced Grant (AdG), PE6, ERC-2016-ADG
Summary As more and more economic activity is moving to the Internet, familiar economic mechanisms are being deployed
at unprecedented scales of size, speed, and complexity. In many cases this new complexity becomes the defining
feature of the deployed economic mechanism and the quantitative difference becomes a key qualitative one.
A well-studied example of such situations is how the humble single-item auction suddenly becomes a
billion-times repeated online ad auction, or even becomes a combinatorial auction with exponentially
many possible outcomes. Similar complexity explosions occur with various markets, with information
dissemination, with pricing structures, and with many other economic mechanisms.
The aim of this proposal is to study the role and implications of such complexity and to start
developing a coherent economic theory that can handle it. We aim to identify various measures of
complexity that are crucial bottlenecks and study them. Examples of such complexities include the
amount of access to data, the length of the description of a mechanism, its communication requirements,
the cognitive complexity required from users, and, of course, the associated computational complexity.
On one hand we will attempt finding ways of effectively dealing with complexity when it is needed, and on
the other hand, attempt avoiding complexity, when possible, replacing it with ``simple'' alternatives
without incurring too large of a loss.
Summary
As more and more economic activity is moving to the Internet, familiar economic mechanisms are being deployed
at unprecedented scales of size, speed, and complexity. In many cases this new complexity becomes the defining
feature of the deployed economic mechanism and the quantitative difference becomes a key qualitative one.
A well-studied example of such situations is how the humble single-item auction suddenly becomes a
billion-times repeated online ad auction, or even becomes a combinatorial auction with exponentially
many possible outcomes. Similar complexity explosions occur with various markets, with information
dissemination, with pricing structures, and with many other economic mechanisms.
The aim of this proposal is to study the role and implications of such complexity and to start
developing a coherent economic theory that can handle it. We aim to identify various measures of
complexity that are crucial bottlenecks and study them. Examples of such complexities include the
amount of access to data, the length of the description of a mechanism, its communication requirements,
the cognitive complexity required from users, and, of course, the associated computational complexity.
On one hand we will attempt finding ways of effectively dealing with complexity when it is needed, and on
the other hand, attempt avoiding complexity, when possible, replacing it with ``simple'' alternatives
without incurring too large of a loss.
Max ERC Funding
2 026 706 €
Duration
Start date: 2017-05-01, End date: 2022-04-30
Project acronym COMPLEX REASON
Project The Parameterized Complexity of Reasoning Problems
Researcher (PI) Stefan Szeider
Host Institution (HI) TECHNISCHE UNIVERSITAET WIEN
Call Details Starting Grant (StG), PE6, ERC-2009-StG
Summary Reasoning, to derive conclusions from facts, is a fundamental task in Artificial Intelligence, arising in a wide range of applications from Robotics to Expert Systems. The aim of this project is to devise new efficient algorithms for real-world reasoning problems and to get new insights into the question of what makes a reasoning problem hard, and what makes it easy. As key to novel and groundbreaking results we propose to study reasoning problems within the framework of Parameterized Complexity, a new and rapidly emerging field of Algorithms and Complexity. Parameterized Complexity takes structural aspects of problem instances into account which are most significant for empirically observed problem-hardness. Most of the considered reasoning problems are intractable in general, but the real-world context of their origin provides structural information that can be made accessible to algorithms in form of parameters. This makes Parameterized Complexity an ideal setting for the analysis and efficient solution of these problems. A systematic study of the Parameterized Complexity of reasoning problems that covers theoretical and empirical aspects is so far outstanding. This proposal sets out to do exactly this and has therefore a great potential for groundbreaking new results. The proposed research aims at a significant impact on the research culture by setting the grounds for a closer cooperation between theorists and practitioners.
Summary
Reasoning, to derive conclusions from facts, is a fundamental task in Artificial Intelligence, arising in a wide range of applications from Robotics to Expert Systems. The aim of this project is to devise new efficient algorithms for real-world reasoning problems and to get new insights into the question of what makes a reasoning problem hard, and what makes it easy. As key to novel and groundbreaking results we propose to study reasoning problems within the framework of Parameterized Complexity, a new and rapidly emerging field of Algorithms and Complexity. Parameterized Complexity takes structural aspects of problem instances into account which are most significant for empirically observed problem-hardness. Most of the considered reasoning problems are intractable in general, but the real-world context of their origin provides structural information that can be made accessible to algorithms in form of parameters. This makes Parameterized Complexity an ideal setting for the analysis and efficient solution of these problems. A systematic study of the Parameterized Complexity of reasoning problems that covers theoretical and empirical aspects is so far outstanding. This proposal sets out to do exactly this and has therefore a great potential for groundbreaking new results. The proposed research aims at a significant impact on the research culture by setting the grounds for a closer cooperation between theorists and practitioners.
Max ERC Funding
1 421 130 €
Duration
Start date: 2010-01-01, End date: 2014-12-31
Project acronym COMPMUSIC
Project Computational models for the discovery of the world's music
Researcher (PI) Francesc Xavier Serra Casals
Host Institution (HI) UNIVERSIDAD POMPEU FABRA
Call Details Advanced Grant (AdG), PE6, ERC-2010-AdG_20100224
Summary Current IT research does not respond to the world's multi-cultural reality. It could be argued that we are imposing the paradigms of our market-driven western culture also on IT and that current IT research results will only facilitate the access of a small part of the world’s information to a small part of the world's population. Most IT research is being carried out with a western centred approach and as a result, our data models, cognition models, user models, interaction models, ontologies, … are all culturally biased. This fact is quite evident in music information research, since, despite the world's richness in musical cultures, most of the research is centred on CDs and metadata of our western commercial music. CompMusic wants to break this huge research bias. By approaching musical information modelling from a multicultural perspective it aims at advancing our state of the art while facilitating the discovery and reuse of the music produced outside the western commercial context. But the development of computational models to address the world’s music information richness cannot be done from the West looking out; we have to involve researchers and musical experts immersed in the different cultures. Their contribution is fundamental to develop the appropriate multicultural musicological and cognitive frameworks from which we should then carry our research on finding appropriate musical features, ontologies, data representations, user interfaces and user centred approaches. CompMusic will investigate some of the most consolidated non-western classical music traditions, Indian (hindustani, carnatic), Turkish-Arab (ottoman, andalusian), and Chinese (han), developing the needed computational models to bring their music into the current globalized information framework. Using these music cultures as case studies, cultures that are alive and have a strong influence in current society, we can develop rich information models that can take advantage of the existing information coming from musicological and cultural studies, from mature performance practice traditions and from active social contexts. With this approach we aim at challenging the current western centred information paradigms, advance our IT research, and contribute to our rich multicultural society.
Summary
Current IT research does not respond to the world's multi-cultural reality. It could be argued that we are imposing the paradigms of our market-driven western culture also on IT and that current IT research results will only facilitate the access of a small part of the world’s information to a small part of the world's population. Most IT research is being carried out with a western centred approach and as a result, our data models, cognition models, user models, interaction models, ontologies, … are all culturally biased. This fact is quite evident in music information research, since, despite the world's richness in musical cultures, most of the research is centred on CDs and metadata of our western commercial music. CompMusic wants to break this huge research bias. By approaching musical information modelling from a multicultural perspective it aims at advancing our state of the art while facilitating the discovery and reuse of the music produced outside the western commercial context. But the development of computational models to address the world’s music information richness cannot be done from the West looking out; we have to involve researchers and musical experts immersed in the different cultures. Their contribution is fundamental to develop the appropriate multicultural musicological and cognitive frameworks from which we should then carry our research on finding appropriate musical features, ontologies, data representations, user interfaces and user centred approaches. CompMusic will investigate some of the most consolidated non-western classical music traditions, Indian (hindustani, carnatic), Turkish-Arab (ottoman, andalusian), and Chinese (han), developing the needed computational models to bring their music into the current globalized information framework. Using these music cultures as case studies, cultures that are alive and have a strong influence in current society, we can develop rich information models that can take advantage of the existing information coming from musicological and cultural studies, from mature performance practice traditions and from active social contexts. With this approach we aim at challenging the current western centred information paradigms, advance our IT research, and contribute to our rich multicultural society.
Max ERC Funding
2 443 200 €
Duration
Start date: 2011-07-01, End date: 2017-06-30
Project acronym COMPUSAPIEN
Project Computing Server Architecture with Joint Power and Cooling Integration at the Nanoscale
Researcher (PI) David ATIENZA ALONSO
Host Institution (HI) ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE
Call Details Consolidator Grant (CoG), PE6, ERC-2016-COG
Summary The soaring demand for computing power in the last years has grown faster than semiconductor technology evolution can sustain, and has produced as collateral undesirable effect a surge in power consumption and heat density in computing servers. Although computing servers are the foundations of the digital revolution, their current designs require 30-40% of the energy supplied to be dissipated in cooling. The remaining energy is used for computation, but their complex many-core designs produce very high operating temperatures. Thus, operating all the cores continuously at maximum performance levels results in system overheating and failures. This situation is limiting the benefits of technology scaling.
The COMPUSAPIEN proposal aims to completely revise the current computing server architecture. In particular, inspired by the mammalian brain, this proposal targets to design a disruptive three-dimensional (3D) computing server architecture that overcomes the prevailing worst-case power and cooling provisioning paradigm for servers. This new 3D server design champions a heterogeneous many-core architecture template with an integrated on-chip microfluidic fuel cell network for joint cooling delivery and power supply. Also, it will include a novel predictive controller based on holistic power-temperature models, which exploit the server software stack to achieve energy-scalable computing capabilities. Because of its integrated electronic-electrochemical architecture design, COMPUSAPIEN is clearly a high-risk high-reward proposal that will bring drastic energy savings with respect to current server design approaches, and will guarantee energy scalability in future server architectures. To realize this vision, COMPUSAPIEN will develop and integrate breakthrough innovations in heterogeneous computing architectures, cooling-power subsystem design, combined microfluidic power delivery and temperature management in computers.
Summary
The soaring demand for computing power in the last years has grown faster than semiconductor technology evolution can sustain, and has produced as collateral undesirable effect a surge in power consumption and heat density in computing servers. Although computing servers are the foundations of the digital revolution, their current designs require 30-40% of the energy supplied to be dissipated in cooling. The remaining energy is used for computation, but their complex many-core designs produce very high operating temperatures. Thus, operating all the cores continuously at maximum performance levels results in system overheating and failures. This situation is limiting the benefits of technology scaling.
The COMPUSAPIEN proposal aims to completely revise the current computing server architecture. In particular, inspired by the mammalian brain, this proposal targets to design a disruptive three-dimensional (3D) computing server architecture that overcomes the prevailing worst-case power and cooling provisioning paradigm for servers. This new 3D server design champions a heterogeneous many-core architecture template with an integrated on-chip microfluidic fuel cell network for joint cooling delivery and power supply. Also, it will include a novel predictive controller based on holistic power-temperature models, which exploit the server software stack to achieve energy-scalable computing capabilities. Because of its integrated electronic-electrochemical architecture design, COMPUSAPIEN is clearly a high-risk high-reward proposal that will bring drastic energy savings with respect to current server design approaches, and will guarantee energy scalability in future server architectures. To realize this vision, COMPUSAPIEN will develop and integrate breakthrough innovations in heterogeneous computing architectures, cooling-power subsystem design, combined microfluidic power delivery and temperature management in computers.
Max ERC Funding
1 999 281 €
Duration
Start date: 2017-06-01, End date: 2022-05-31
