A generic implementation of a quantified predictor on FPGAs

Predictors are used in many fields of computer architectures to enhance performance. With good estimations of future system behaviour, policies can be developed to improve system performance or reduce power consumption. These policies become more effective if the predictors are implemented in hardware and can provide quantified forecasts and not only binary ones. In this paper, we present and evaluate a generic predictor implemented in VHDL running on an FPGA which produces quantified forecasts. Moreover, a complete scalability analysis is presented which shows that our implementation has a maximum device utilization of less than 5%. Furthermore, we analyse the power consumption of the predictor running on an FPGA. Additionally, we show that this implementation can be clocked by over 210 MHz. Finally, we evaluate a power-saving policy based on our hardware predictor. Based on predicted idle periods, this power-saving policy uses power-saving modes and is able to reduce memory power consumption by 14.3%

Real-Time Vision System for License Plate Detection and Recognition on FPGA

Rapid development of the Field Programmable Gate Array (FPGA) offers an alternative way to provide acceleration for computationally intensive tasks such as digital signal and image processing. Its ability to perform parallel processing shows the potential in implementing a high speed vision system. Out of numerous applications of computer vision, this paper focuses on the hardware implementation of one that is commercially known as Automatic Number Plate Recognition (ANPR).Morphological operations and Optical Character Recognition (OCR) algorithms have been implemented on a Xilinx Zynq-7000 All-Programmable SoC to realize the functions of an ANPR system. Test results have shown that the designed and implemented processing pipeline that consumed 63 % of the logic resources is capable of delivering the results with relatively low error rate. Most importantly, the computation time satisfies the real-time requirement for many ANPR applications

A Methodology for Predicting Application-Specific Achievable Memory Bandwidth for HW/SW-Codesign

The trend of using heterogeneous computing and HW/SW-Codesign approaches allows increasing performance significantly while reducing power consumption. One of the main challenges when combining multiple processing devices is the communication, as an inefficient communication configuration can pose a bottleneck to the overall system performance. To address this problem, we present a methodology that assists the designer in making good design decisions for systems using shared DDR memory for communication. Our methodology analyzes a software implementation of the application and subsequently predicts the memory accesses of a functionally equivalent hardware implementation of the selected function. We furthermore propose an IP core that can perform these predicted memory accesses to estimate the achievable memory bandwidth between a functionally equivalent hardware implementation and shared memory. The resulting achievable memory bandwidth estimations differ by less than 2% from the actual achievable memory bandwidth of a functionally equivalent hardware implementation, demonstrating the feasibility of the presented methodology

An efficient and flexible FPGA implementation of a face detection system

This paper proposes a hardware architecture based on the object detection system of Viola and Jones using Haar-like features. The proposed design is able to discover faces in real-time with high accuracy. Speed-up is achieved by exploiting the parallelism in the design, where multiple classifier cores can be added. To maintain a flexible design, classifier cores can be assigned to different images. Moreover using different training data, every core is able to detect a different object type. As development platform, the Zynq-7000 SoC from Xilinx is used, which features an ARM Cortex-A9 dual-core CPU and a programmable logic (FPGA). The current implementation focuses on the face detection and achieves a real-time detection at the rate of 16.53 FPS on image resolution of 640×480 pixels, which represents a speed-up of 6.46 times compared to the equivalent OpenCV software solution

An Integrated Hardware-Software Approach to Task Graph Management

Task-based parallel programming models with explicit data dependencies, such as OmpSs, are gaining popularity, due to the ease of describing parallel algorithms with complex and irregular dependency patterns. These advantages, however, come at a steep cost of runtime overhead incurred by dynamic dependency resolution. Hardware support for task management has been proposed in previous work as a possible solution. We present VSs, a runtime library for the OmpSs programming model that integrates the Nexus++ hardware task manager, and evaluate the performance of the VSs-Nexus++ system. Experimental results show that applications with fine-grain tasks can achieve speedups of up to 3.4×, while applications optimized for current runtimes attain 1.3×. Providing support for hardware task managers in runtime libraries is therefore a viable approach to improve the performance of OmpSs applications

Nexus#: a distributed hardware task manager for task-based programming models

In the era of multicore systems, it is expected that the number of cores that can be integrated on a single chip will be 3-digit. The key to utilize such a huge computational power is to extract the very fine parallelism in the user program. This is non-trivial for the average programmer, and becomes very hard as the number of potential parallel instances increases. Task-based programming models such as OmpSs are promising, since they handle the detection of dependencies and synchronization for the programmer. However, state-of-the-art research shows that task management is not cheap, and introduces a significant overhead that limits the scalability of OmpSs. Nexus# is a hardware accelerator for the OmpSs runtime system, which dynamically monitors dependencies between tasks. It is fully synthesizable in VHDL, and has a distributed task graph model to achieve the best scalability. Supporting tasks with arbitrary number of parameters and any dependency pattern, Nexus# achieves better performance than Nanos, the official OmpSs runtime system, and scales well for the H264dec benchmark with very fine grained tasks, among other benchmarks from the Starbench suite

Proximity Scheme for Instruction Caches in Tiled CMP Architectures

Recent research results show that there is a high degree of code sharing between cores in multi-core architectures. In this paper we propose a proximity scheme for the instruction caches, a scheme in which the shared code blocks among the neighbouring L2 caches in tiled multi-core architectures are exploited to reduce the average cache miss penalty and the on-chip network traffic. We evaluate the proposed proximity scheme for instruction caches using a full-system simulator running an n-core tiled CMP. The experimental results reveal a significant execution time improvement of up to 91.4% for microbenchmarks whose instruction footprint does not fit in the private L2 cache. For real applications from the PARSEC benchmarks suite, the proposed scheme results in speedups of up to 8%

A Quantitative Analysis of the Memory Architecture of FPGA-SoCs

In recent years, so called FPGA-SoCs have been introduced by Intel (formerly Altera) and Xilinx. These devices combine multi-core processors with programmable logic. This paper analyzes the various memory and communication interconnects found in actual devices, particularly the Zynq-7020 and Zynq-7045 from Xilinx and the Cyclone V SE SoC from Intel. Issues such as different access patterns, cache coherence and full-duplex communication are analyzed, for both generic accesses as well as for a real workload from the field of video coding. Furthermore, the paper shows that by carefully choosing the memory interconnect networks as well as the software interface, high-speed memory access can be achieved for various scenarios

GPGPU workload characteristics and performance analysis

GPUs are much more power-efficient devices compared to CPUs, but due to several performance bottlenecks, the performance per watt of GPUs is often much lower than what could be achieved theoretically. To sustain and continue high performance computing growth, new architectural and application techniques are required to create power-efficient computing systems. To find such techniques, however, it is necessary to study the power consumption at a detailed level and understand the bottlenecks which cause low performance. Therefore, in this paper, we study GPU power consumption at component level and investigate the bottlenecks that cause low performance and low energy efficiency. We divide the low performance kernels into low occupancy and full occupancy categories. For the low occupancy category, we study if increasing the occupancy helps in increasing performance and energy efficiency. For the full occupancy category, we investigate if these kernels are limited by memory bandwidth, coalescing efficiency, or SIMD utilization.EC/FP7/288653/EU/Low-Power Parallel Computing on GPUs/LPGP

Architecture Exploration Based on GA-PSO Optimization, ANN Modeling, and Static Scheduling

Embedded systems are widely used today in different digital signal processing (DSP) applications that usually require high computation power and tight constraints. The design space to be explored depends on the application domain and the target platform. A tool that helps explore different architectures is required to design such an efficient system. This paper proposes an architecture exploration framework for DSP applications based on Particle Swarm Optimization (PSO) and genetic algorithms (GA) techniques that can handle multiobjective optimization problems with several hybrid forms. A novel approach for performance evaluation of embedded systems is also presented. Several cycle-accurate simulations are performed for commercial embedded processors. These simulation results are used to build an artificial neural network (ANN) model that can predict performance/power of newly generated architectures with an accuracy of 90% compared to cycle-accurate simulations with a very significant time saving. These models are combined with an analytical model and static scheduler to further increase the accuracy of the estimation process. The functionality of the framework is verified based on benchmarks provided by our industrial partner ON Semiconductor to illustrate the ability of the framework to investigate the design space