An energy‐aware performance analysis of SWIMM: Smith–Waterman implementation on Intel's Multicore and Manycore architectures

Alignment is essential in many areas such as biological, chemical and criminal forensics. The well‐known Smith–Waterman (SW) algorithm is able to retrieve the optimal local alignment with quadratic time and space complexity. There are several implementations that take advantage of computing parallelization, such as manycores, FPGAs or GPUs, in order to reduce the alignment effort. In this research, we adapt, develop and tune the SW algorithm named SWIMM on a heterogeneous platform based on Intel's Xeon and Xeon Phi coprocessor. SWIMM is a free tool available in a public git repository https://github.com/enzorucci/SWIMM. We efficiently exploit data and thread‐level parallelism, reaching up to 380 GCUPS on heterogeneous architecture, 350 GCUPS for the isolated Xeon and 50 GCUPS on Xeon Phi. Despite the heterogeneous implementation obtaining the best performance, it is also the most energy‐demanding. In fact, we also present a trade‐off analysis between performance and power consumption. The greenest configuration is based on an isolated multicore system that exploits AVX2 instruction set architecture reaching 1.5 GCUPS/Watts.Facultad de Informátic

The effect of coefficient quantization optimization on filtering performance and gate count

Abstract. Digital filters are an essential component of Digital Signal Processing (DSP) applications and play a crucial role in removing unwanted signal components from a desired signal. However, digital filters are known to be resource-intensive and consume a large amount of power, making it important to optimize their design in order to minimize hardware requirements such as multipliers, adders, and registers. This trade-off between filter performance and hardware consumption can be influenced by the quantization of filter coefficients. Therefore, this thesis investigates the quantization of Finite Impulse Response (FIR) filter coefficients and analyzes its impact on filter performance and hardware resource consumption. A method called dynamic quantization is introduced and an algorithm for step-by-step dynamic quantization is provided to improve upon the results obtained with the classical fixed point quantization method. To demonstrate the effectiveness of this approach, the dynamic quantization of filter coefficients for a Low-pass Equiripple FIR filter is examined and a comparative study of the magnitude response and hardware consumption of the generated filter using both the classical and dynamic quantization methods is presented. By understanding the trade-offs and benefits of each quantization method, engineers can make informed decisions about the most appropriate approach for their specific application

Performance Analysis of Montgomery Multiplier using 32nm CNTFET Technology

In VLSI design vacillating the parameters results in variation of critical factors like area, power and delay. The dominant sources of power dissipation in digital systems are the digital multipliers. A digital multiplier plays a major role in a mixture of arithmetic operations in digital signal processing applications hinge on add and shift algorithms. In order to accomplish high execution speed, parallel array multipliers are comprehensively put into application. The crucial drawback of these multipliers is that it exhausts more power than any other multiplier architectures. Montgomery Multiplication is the popularly used algorithm as it is the most efficient technique to perform arithmetic based calculations. A high-speed multiplier is greatly coveted for its extraordinary leverage. The primary blocks of a multiplier are basically comprised of adders. Thus, in order to attain a significant reduction in power consumption at the chip level the power utilization in adders can be decreased. To obtain desired results in performance parameters of the multiplier an efficient and dynamic adder is proposed and incorporated in the Montgomery multiplier. The Carbon Nanotube field effect transistor (CNTFET) is a promising new device that may supersede some of the fundamental limitations of a silicon based MOSFET. The architecture has been designed in 130nm and 32nm CMOS and CNTFET technology in Synopsys HSpice. The analysed parameters that are considered in determining the performance are power delay product, power and delay and comparison is made with both the technologies.The simulation results of this paper affirmed the CNTFET based Montgomery multiplier improved power consumption by 76.47% ,speed by 72.67% and overall energy by 67.76% as compared to MOSFET-based Montgomery multiplier