Exploiting partial reconfiguration through PCIe for a microphone array network emulator

The current Microelectromechanical Systems (MEMS) technology enables the deployment of relatively low-cost wireless sensor networks composed of MEMS microphone arrays for accurate sound source localization. However, the evaluation and the selection of the most accurate and power-efficient network’s topology are not trivial when considering dynamic MEMS microphone arrays. Although software simulators are usually considered, they consist of high-computational intensive tasks, which require hours to days to be completed. In this paper, we present an FPGA-based platform to emulate a network of microphone arrays. Our platform provides a controlled simulated acoustic environment, able to evaluate the impact of different network configurations such as the number of microphones per array, the network’s topology, or the used detection method. Data fusion techniques, combining the data collected by each node, are used in this platform. The platform is designed to exploit the FPGA’s partial reconfiguration feature to increase the flexibility of the network emulator as well as to increase performance thanks to the use of the PCI-express high-bandwidth interface. On the one hand, the network emulator presents a higher flexibility by partially reconfiguring the nodes’ architecture in runtime. On the other hand, a set of strategies and heuristics to properly use partial reconfiguration allows the acceleration of the emulation by exploiting the execution parallelism. Several experiments are presented to demonstrate some of the capabilities of our platform and the benefits of using partial reconfiguration

Rapid-Prototyping Emulation System for Embedded System Hardware Verification, using a SystemC Control System Environment and Reconfigurable Multimedia Hardware Development Platform

This paper describes research into the suitability of using SystemC for rapid prototyping of embedded systems. SystemC[1][2][3] communication interface protocols [4][5] are interfaced with a reconfigurable hardware system platform to provide a real-time emulation environment, allowing SystemC simulations to be directly translated into real-time solutions. The consequent Rapid Prototyping Emulation System Platform1, suitable for the implementation of consumer level multimedia systems, is described, including the system architecture, SystemC Controller model, the FPGA configured MicroBlaze CPU system and additional logic devices implemented on the Multimedia development board used for the hardware in the PESP, illustrated in the context of a typical application

Special Session on Industry 4.0

No abstract available

Using Partial Reconfiguration for SoC Design and Implementation

Most reconfigurable systems rely on FPGA technology. Among these ones, those which permit dynamic and partial reconfiguration, offer added benefits in flexibility, in-field device upgrade, improved design and manufacturing time, and even, in some cases, power consumption reductions. However, dynamic reconfiguration is a complex task, and the real benefits of its use in real applications have been often questioned. This paper presents an overview of the partial reconfiguration technique application, along with four original applications. The main goal of these applications is to test several architectures with different flexibility and, to search for the partial reconfiguration "killing application", that is, the application that better demonstrates the benefits of today reconfigurable systems based on commercial FPGAs. Therefore, the presented applications are rather a proof of concept, than fully operative and closed systems. First, a brief introduction to the partial reconfigurable systems application topic has been included. After that, the descriptions of the created reconfigurable systems are presented: first, an on-chip communications emulation framework, second, an on chip debugging system, third, a wireless sensor network reconfigurable node and finally, a remote reconfigurable client-server device. Each application is described in a separate section of the paper along with some test and results. General conclusions are included at the end of the pape

Comprehensive Evaluation of OpenCL-based Convolutional Neural Network Accelerators in Xilinx and Altera FPGAs

Deep learning has significantly advanced the state of the art in artificial intelligence, gaining wide popularity from both industry and academia. Special interest is around Convolutional Neural Networks (CNN), which take inspiration from the hierarchical structure of the visual cortex, to form deep layers of convolutional operations, along with fully connected classifiers. Hardware implementations of these deep CNN architectures are challenged with memory bottlenecks that require many convolution and fully-connected layers demanding large amount of communication for parallel computation. Multi-core CPU based solutions have demonstrated their inadequacy for this problem due to the memory wall and low parallelism. Many-core GPU architectures show superior performance but they consume high power and also have memory constraints due to inconsistencies between cache and main memory. FPGA design solutions are also actively being explored, which allow implementing the memory hierarchy using embedded BlockRAM. This boosts the parallel use of shared memory elements between multiple processing units, avoiding data replicability and inconsistencies. This makes FPGAs potentially powerful solutions for real-time classification of CNNs. Both Altera and Xilinx have adopted OpenCL co-design framework from GPU for FPGA designs as a pseudo-automatic development solution. In this paper, a comprehensive evaluation and comparison of Altera and Xilinx OpenCL frameworks for a 5-layer deep CNN is presented. Hardware resources, temporal performance and the OpenCL architecture for CNNs are discussed. Xilinx demonstrates faster synthesis, better FPGA resource utilization and more compact boards. Altera provides multi-platforms tools, mature design community and better execution times