Dataflow-based Design and Implementation of Image Processing Applications

Dataflow is a well known computational model and is widely used for expressing the functionality of digital signal processing (DSP) applications, such as audio and video data stream processing, digital communications, and image processing. These applications usually require real-time processing capabilities and have critical performance constraints. Dataflow provides a formal mechanism for describing specifications of DSP applications, imposes minimal data-dependency constraints in specifications, and is effective in exposing and exploiting task or data level parallelism for achieving high performance implementations. To demonstrate dataflow-based design methods in a manner that is concrete and easily adapted to different platforms and back-end design tools, we present in this report a number of case studies based on the lightweight dataflow (LWDF) programming methodology. LWDF is designed as a "minimalistic" approach for integrating coarse grain dataflow programming structures into arbitrary simulation- or platform-oriented languages, such as C, C++, CUDA, MATLAB, SystemC, Verilog, and VHDL. In particular, LWDF requires minimal dependence on specialized tools or libraries. This feature --- together with the rigorous adherence to dataflow principles throughout the LWDF design framework --- allows designers to integrate and experiment with dataflow modeling approaches relatively quickly and flexibly into existing design methodologies and processes

Adaptive tracking of people and vehicles using mobile platforms

Tracking algorithms have important applications in detection of humans and vehicles for border security and other areas. For large-scale deployment of such algorithms, it is critical to provide methods for their cost- and energy-efficient realization. To this end, commodity mobile devices have significant potential for use as prototyping and testing platforms due to their low cost, widespread availability, and integration of advanced communications, sensing, and processing features. Prototypes developed on mobile platforms can be tested, fine-tuned, and demonstrated in the field and then provide reference implementations for application-specific disposable sensor node implementations that are targeted for deployment. In this paper, we develop a novel, adaptive tracking system that is optimized for energy-efficient, real-time operation on off-the-shelf mobile platforms. Our tracking system applies principles of dynamic data-driven application systems (DDDAS) to periodically monitor system operating characteristics and apply these measurements to dynamically adapt the specific classifier configurations that the system employs. Our resulting adaptive approach enables powerful optimization of trade-offs among energy consumption, real-time performance, and tracking accuracy based on time-varying changes in operational characteristics. Through experiments employing an Android-based tablet platform, we demonstrate the efficiency of our proposed tracking system design for multimode detection of human and vehicle targets.publishedVersionPeer reviewe

Dataflow-Based Implementation of Deep Learning Application

The proliferation of research on high efficient performance on deep learning has contributed to an increasing challenge and interest in the topic concerning the integration of this advanced-technology into daily life. Although a large amount of work on the domain of machine learning has been dedicated to the accuracy, efficiency, net topology and algorithm in the training and recognition procedures, the investigation of deep learning implementations in highly resource-constrainted contexts has been relatively unexplored due to the large computational requirements involved during the process of training large-scale network. In light of this, one process concentrated on parameters extraction and dataflow design, implementation, optimization of one deep learning application for vehicle classification on multicore platforms with limited numbers of available processor cores is demonstrated. By means of thousands of actors computation and fifos communication, we establish one enormous and complex dataflow graph, and then using the resulting dataflow representations, we apply a wide range of design optimizations to probe efficient implementations on three different multicore platforms. Through the incorporation of dataflow techniques, it is gratifying for us to see its effectiveness and efficiency in the several flexible experiments with alternative platforms that tailored to the resource constraints. Besides, we pioneer three general, novel, primitive and thorough flow charts during the work - deep leanring model, LIDE-C establishing model, LIDE-C coding model. Finally, not only LIDE-C we utilize for the implementation, but also DICE we apply for validation and verification. Both tools are incubated by DSPCAD at Maryland of University, and will be updated better in the future