AMONG different arithmetic blocks, the multiplier is one of the main blocks, which is widely used in different applications especially signal processing applications. There are two general architectures for the multipliers, which are sequential and parallel. While sequential architectures are low power, their latency is very large. On the other hand, parallel architectures (such as Wallace tree and Dadda) are fast while having high-power consumptions. The parallel multipliers are used in high-performance applications where their large power consumptions may create hot-spot locations on the die. Since the power consumption and speed are critical parameters in the design of digital circuits, the optimizations of these parameters for multipliers become critically important. Very often, the optimization of one parameter is performed considering a constraint for the other parameter. Specifically, achieving the desired performance (speed) considering the limited power budget of portable systems is challenging task. In addition, having a given level of reliability may be another obstacle in reaching the system target performance.
To meet the power and speed specifications, a variety of methods at different design abstraction levels have been suggested. Approximate computing approaches are based on achieving the target specifications at the cost of reducing the computation accuracy. The approach may be used for applications where there is not a unique answer and/or a set of answers near the accurate result can be considered accept- able. These applications include multimedia processing, machine learning, signal processing, and other error resilient computations. Approximate arithmetic units are mainly based on the simplification of the arithmetic units circuits. There are many prior works focusing on approximate multipliers, which provide higher speeds and lower power consumptions at the cost of lower accuracies. Almost, all of the proposed approximate multipliers are based on having a fixed level of accuracy during the runtime. The runtime accuracy re-configurability, however, is considered as a useful feature for providing different levels of quality of service during the system operation. Here, by reducing the quality (accuracy), the delay and/or power consumption of the unit may be reduced. In addition, some digital systems, such as general purpose processors, may be utilized for both approximate and exact computation modes. An approach for achieving this feature is to use an approximate unit along with a corresponding correction unit. The correction unit, however, increases the delay, power, and area overhead of the circuit. Also, the error correction procedure may require more than one clock cycle, which could, in turn, slow down the processing further.
- Not provided the output with Chrominance
- More Area and power
- Low Performance
In this paper, we propose four 4:2 compressors, which have the flexibility of switching between the exact and approximate operating modes. In the approximate mode, these dual-quality compressors provide higher speeds and lower power consumptions at the cost of lower accuracy. Each of these compressors has its own level of accuracy in the approximate mode as well as different delays and power dissipations in the approximate and exact modes. Using these compressors in the structures of parallel multipliers provides configurable multipliers whose accuracies (as well as their powers and speeds) may change dynamically during the runtime. The proposed multiplier saves few adder circuits in partial products, and this proposed multiplier is evaluated with an image processing application. In existing thing, to using this multiplier to design image processing evaluation on only luminance based application, but here the proposed work is modified with Gaussian noise reduction with luminance and chrominance based application, this design to implemented in VHDL, and synthesized in Xilinx S6LX9 FPGA and shown the power, area and delay reports.
- Provide the output with luminance and chrominance
- Less Area and Power
- High Performance