scispace - formally typeset
Search or ask a question
Author

Shuvra S. Bhattacharyya

Bio: Shuvra S. Bhattacharyya is an academic researcher from University of Maryland, College Park. The author has contributed to research in topics: Dataflow & Scheduling (computing). The author has an hindex of 37, co-authored 404 publications receiving 6243 citations. Previous affiliations of Shuvra S. Bhattacharyya include Tampere University of Technology & Indian Institute of Technology Kanpur.


Papers
More filters
Book
01 Jan 2000
TL;DR: This work presents architectures and design methodologies for parallel systems in embedded DSP applications, and describes unique techniques for optimizing communication and synchronization.
Abstract: Showing how to design multiprocessor computer systems that are streamlined for multimedia applications, this work presents architectures and design methodologies for parallel systems in embedded DSP applications. It describes unique techniques for optimizing communication and synchronization and provides several examples of practical applications that demonstrate the relevance of the techniques presented. This second edition updates the background material on existing embedded multiprocessors, including single-chip multiprocessors. It also summarizes the new research on dataflow models for signal processing that has been carried out since the publication of the first edition.

569 citations

Journal ArticleDOI
TL;DR: This paper develops precise, formal semantics for parameterized synchronous dataflow (PSDF), the application of the parameterized modeling framework to SDF that allows data-dependent, dynamic DSP systems to be modeled in a natural and intuitive fashion.
Abstract: Dataflow has proven to be an attractive computation model for programming digital signal processing (DSP) applications. A restricted version of dataflow, termed synchronous dataflow (SDF), that offers strong compile-time predictability properties, but has limited expressive power, has been studied extensively in the DSP context. Many extensions to synchronous dataflow have been proposed to increase its expressivity while maintaining its compile-time predictability properties as much as possible. We proposed a parameterized dataflow framework that can be applied as a meta-modeling technique to significantly improve the expressive power of any dataflow model that possesses a well-defined concept of a graph iteration, Indeed, the parameterized dataflow framework is compatible with many of the existing dataflow models for DSP including SDF, cyclo-static dataflow, scalable synchronous dataflow, and Boolean dataflow. In this paper, we develop precise, formal semantics for parameterized synchronous dataflow (PSDF)-the application of our parameterized modeling framework to SDF-that allows data-dependent, dynamic DSP systems to be modeled in a natural and intuitive fashion. Through our development of PSDF, we demonstrate that desirable properties of a DSP modeling environment such as dynamic reconfigurability and design reuse emerge as inherent characteristics of our parameterized framework. An example of a speech compression application is used to illustrate the efficacy of the PSDF approach and its amenability to efficient software synthesis techniques. In addition, we illustrate the generality of our parameterized framework by discussing its application to cyclo-static dataflow, which is a popular alternative to the SDF model.

250 citations

Journal ArticleDOI
01 Jun 1999
TL;DR: This paper reviews a set of algorithms for compiling dataflow programs for embedded DSP applications into efficient implementations on programmable digital signal processors that focus primarily on the minimization of code size, and the minimizing of the memory required for the buffers that implement the communication channels in the input dataflow graph.
Abstract: The implementation of software for embedded digital signal processing (DSP) applications is an extremely complex process. The complexity arises from escalating functionality in the applicationss intense time-to-market pressuress and stringent cost, power and speed constraints. To help cope with such complexity, DSP system designers have increasingly been employing high-level, graphical design environments in which system specification is based on hierarchical dataflow graphs. Consequently, a significant industry has emerged for the development of data-flow-based DSP design environments. Leading products in this industry include SPW from Cadence, COSSAP from Synopsys, ADS from Hewlett Packard, and DSP Station from Mentor Graphics. This paper reviews a set of algorithms for compiling dataflow programs for embedded DSP applications into efficient implementations on programmable digital signal processors. The algorithms focus primarily on the minimization of code size, and the minimization of the memory required for the buffers that implement the communication channels in the input dataflow graph. These are critical problems because programmable digital signal processors have very limited amounts of on-chip memory, and the speed, power, and cost penalties for using off-chip memory are often prohibitively high for embedded applications. Furthermore, memory demands of applications are increasing at a significantly higher rate than the rate of increase in on-chip memory capacity offered by improved integrated circuit technology.

234 citations

Journal ArticleDOI
TL;DR: An attention-aided CNN model based on the traditional CNN model that incorporates attention modules to aid networks that focus on more discriminative channels or positions for spectral and spatial classifications of hyperspectral images is proposed.
Abstract: Convolutional neural networks (CNNs) have been widely used for hyperspectral image classification. As a common process, small cubes are first cropped from the hyperspectral image and then fed into CNNs to extract spectral and spatial features. It is well known that different spectral bands and spatial positions in the cubes have different discriminative abilities. If fully explored, this prior information will help improve the learning capacity of CNNs. Along this direction, we propose an attention-aided CNN model for spectral–spatial classification of hyperspectral images. Specifically, a spectral attention subnetwork and a spatial attention subnetwork are proposed for spectral and spatial classifications, respectively. Both of them are based on the traditional CNN model and incorporate attention modules to aid networks that focus on more discriminative channels or positions. In the final classification phase, the spectral classification result and the spatial classification result are combined together via an adaptively weighted summation method. To evaluate the effectiveness of the proposed model, we conduct experiments on three standard hyperspectral data sets. The experimental results show that the proposed model can achieve superior performance compared with several state-of-the-art CNN-related models.

185 citations

Proceedings ArticleDOI
05 Jun 2000
TL;DR: A parameterized dataflow framework that can be applied as a meta-modeling technique to significantly improve the expressive power of an arbitrary data-flow model that possesses a well-defined concept of a graph iteration is proposed.
Abstract: Dataflow has proven to be an attractive computation model for programming DSP applications. A restricted version of dataflow, termed synchronous dataflow (SDF), that offers strong compile-time predictability properties, but has limited expressive power, has been studied extensively in the DSP context. Many extensions to synchronous dataflow have been proposed to increase its expressivity, while maintaining its compile-time predictability properties as much as possible. We propose a parameterized data-flow framework that can be applied as a meta-modeling technique to significantly improve the expressive power of an arbitrary data-flow model that possesses a well-defined concept of a graph iteration. Indeed, the parameterized dataflow framework is compatible with many of the existing dataflow models for DSP including SDF, CSDF, and SSDF. We develop a precise, formal semantics for parameterized synchronous dataflow that allows data-dependent dynamic DSP systems to be modeled in a natural and intuitive fashion. Desirable properties of a modeling environment like dynamic re-configurability and design re-use emerge as inherent characteristics of the parameterized framework. An example of a speech compression application is used to illustrate the efficacy of the parameterized modeling techniques in real-life data-dependent DSP systems.

156 citations


Cited by
More filters
Journal ArticleDOI

[...]

08 Dec 2001-BMJ
TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

33,785 citations

Book
27 Dec 1999
TL;DR: The basic principles of evolutionary multiobjective optimization are discussed from an algorithm design perspective and the focus is on the major issues such as fitness assignment, diversity preservation, and elitism in general rather than on particular algorithms.
Abstract: Multiple, often conflicting objectives arise naturally in most real-world optimization scenarios. As evolutionary algorithms possess several characteristics due to which they are well suited to this type of problem, evolution-based methods have been used for multiobjective optimization for more than a decade. Meanwhile evolutionary multiobjective optimization has become established as a separate subdiscipline combining the fields of evolutionary computation and classical multiple criteria decision making. In this paper, the basic principles of evolutionary multiobjective optimization are discussed from an algorithm design perspective. The focus is on the major issues such as fitness assignment, diversity preservation, and elitism in general rather than on particular algorithms. Different techniques to implement these strongly related concepts will be discussed, and further important aspects such as constraint handling and preference articulation are treated as well. Finally, two applications will presented and some recent trends in the field will be outlined.

2,062 citations

Book
23 Jan 2013
TL;DR: This book takes a cyber-physical approach to embedded systems, introducing the engineering concepts underlying embedded systems as a technology and as a subject of study.
Abstract: The most visible use of computers and software is processing information for human consumption. The vast majority of computers in use, however, are much less visible. They run the engine, brakes, seatbelts, airbag, and audio system in your car. They digitally encode your voice and construct a radio signal to send it from your cell phone to a base station. They command robots on a factory floor, power generation in a power plant, processes in a chemical plant, and traffic lights in a city. These less visible computers are called embedded systems, and the software they run is called embedded software. The principal challenges in designing and analyzing embedded systems stem from their interaction with physical processes. This book takes a cyber-physical approach to embedded systems, introducing the engineering concepts underlying embedded systems as a technology and as a subject of study. The focus is on modeling, design, and analysis of cyber-physical systems, which integrate computation, networking, and physical processes. The second edition offers two new chapters, several new exercises, and other improvements. The book can be used as a textbook at the advanced undergraduate or introductory graduate level and as a professional reference for practicing engineers and computer scientists. Readers should have some familiarity with machine structures, computer programming, basic discrete mathematics and algorithms, and signals and systems.

1,017 citations