The complexity of embedded systems is ever increasing, to support wide range of application domains. Dedicated hardware provides improved performance but with limited resources reusability and application scalability. Using higher abstraction methods for hardware development shortens the time to market. The concept of hardware objects (HWO) allows for more efficient hardware resource reuse and scalability of the target application. A single task of the application can be mapped on an appropriate type of hardware structure. A single hardware structure can be shared in time by several application tasks, multiple instances of the same HW structure can be used for concurrent computation. Uniform hardware object structure shortens design time and simplifies design of target system independently on application domain. The system could contain a heterogeneous set of HWO instances to satisfy all application domains.

Advanced image processing algorithms usually require high computing performance. Today's personal computers (PCs) offer satisfying resources for implementation of image processing tasks. However, as the image processing techniques are becoming more and more complex other implementation possibilities have to be searched. Since image processing algorithms usually comply with the Single Instruction Multiple Data (SIMD) model, implementation efforts using such hardware resources are suitable. An example of the SIMD hardware component available nowadays is the graphics processor (GPU) embedded in modern graphics cards manufactured for PCs. In this paper, the implementation of a blind image deconvolution algorithm using graphics processor as the SIMD computing resource is presented. The resulting performance is compared to the performance achieved on a common general-purpose processor (CPU).

The paper describes application of the clock-gating techniques, often used in ASIC designs, to the field of FPGA-based systems. The clock-gating techniques are used to reduce the total power of the system. To achieve this, we reduce clock power consumption of the system by switching-off the clock signal for the parts of system that are not used. The system presented in this paper is based on the main processor, extended with several reconfigurable accelerators. These accelerators extend the processor capabilities by several vector operations and can be reprogrammed in run-time. Clock gating, in our design, is used to switch the accelerators off when not used. As the accelerators can represent a major part of the system size, switching them off can significantly reduce the power consumption. We also propose the method for estimation of the reduction of power consumption that can be achieved using the clock-gating technique.

In 2000 we described a proposal for a logarithmic arithmetic unit, which we suggested would offer a faster, more accurate alternative to floating-point procedures. Would it in fact do so, and could it feasibly be integrated into a microprocessor so that the intended benefits might be realized? Here, we describe the European logarithmic microprocessor, a device designed around that unit, and compare its performance with that of a commercial superscalar pipelined floating-point processor. We conclude that the experiment has been successful, and that for 32-bit work, logarithmic arithmetic may now be the technique of choice.

In modern radio location systems based on passive coherent location (PCL), one of the key problems is an efficient and accurate computation of the cross ambiguity function (CAF). This function is related to the direct signal and signals reflected from localized targets. CAF represent power spectral density distribution of the cross-correlation between direct and reflected signals. In this paper we present an attempt to implement (numerically effective and sufficiently accurate) the CAF computation accelerator on FPGA. The results show that this accelerator could be used to compute CAF in real-time in the future PCL systems. The presented design has been implemented in PC accelerator cards based both on Xilinx Virtex IV and Altera Stratix II devices. The presented contribution gives overall information about the algorithms, accelerator architecture design and achieved performance. The possibilities of the future enhancements are discussed.