Base Paper Abstract:
Approximate computing is a promising paradigm for trading off accuracy to improve hardware efficiency in error-resilient applications such as neural networks and image processing. This brief presents an ultra-efficient approximate multiplier with error compensation capability. The proposed multiplier considers the least significant half of the product a constant compensation term. The other half is calculated precisely to provide an ultra-efficient hardware-accuracy tradeoff. Furthermore, a low-complexity but effective error compensation module (ECM) is presented, significantly improving accuracy. The proposed multiplier is simulated using HSPICE with 7nm tri-gate Fin FET technology. The proposed design significantly improves the energy-delay product, on average, by 77% and 54% compared to the exact and existing approximate designs. Moreover, the proposed multiplier’s accuracy and effectiveness in neural networks and image multiplication are evaluated using MATLAB simulations. The results indicate that the proposed multiplier offers high accuracy comparable to the exact multiplier in NNs and provides an average PSNR of more than 51dB in image multiplication. Accordingly, it can be an effective alternative for exact multipliers in practical error-resilient applications.
List of the following materials will be included with the Downloaded Backup:Base Paper Abstract:
High speed multimedia applications have paved way for a whole new area in high speed error-tolerant circuits with approximate computing. These applications deliver high performance at the cost of reduction in accuracy. Furthermore, such implementations reduce the complexity of the system architecture, delay and power consumption. This paper explores and proposes the design and analysis of two approximate compressors with reduced area, delay and power with comparable accuracy when compared with the existing architectures. The proposed designs are implemented using 45 nm CMOS technology and efficiency of the proposed designs have been extensively verified and projected on scales of area, delay, power, Power Delay Product (PDP), Error Rate (ER), Error Distance (ED), and Accurate Output Count (AOC). The proposed approximate 4 : 2 compressor shows 56.80% reduction in area, 57.20% reduction in power, and 73.30% reduction in delay compared to an accurate 4 : 2 compressor. The proposed compressors are utilised to implement 8 × 8 and 16 × 16 Dadda multipliers. These multipliers have comparable accuracy when compared with state-of-the-art approximate multipliers. The analysis is further extended to project the application of the proposed design in error resilient applications like image smoothing and multiplication.
List of the following materials will be included with the Downloaded Backup:Abstract:
Approximate multipliers attract a large interest in the scientific literature that proposes several circuits built with approximate 4-2 compressors. Due to the large number of proposed solutions, the designer who wishes to use an approximate 4-2 compressor is faced with the problem of selecting the right topology. In this paper, we present a comprehensive survey and comparison of approximate 4-2 compressors previously proposed in literature. We present also a novel approximate compressor, so that a total of twelve different approximate 4-2 compressors are analyzed. The investigated circuits are employed to design 8 × 8 and 16 × 16 multipliers, implemented in 28nm CMOS technology. For each operand size we analyze two multiplier configurations, with different levels of approximations, both signed and unsigned. Our study highlights that there is no unique winning approximate compressor topology since the best solution depends on the required precision, on the signedness of the multiplier and on the considered error metric.
List of the following materials will be included with the Downloaded Backup:Base Paper Abstract:
Approximate computing is an emerging paradigm for trading off computing accuracy to reduce energy consumption and design complexity in a variety of applications, for which exact computation is not a critical requirement. Different from conventional designs using AND-OR and XOR gates, the majority gate is widely used in many emerging nanotechnologies. An ultra-efficient 6-2 compressor is proposed in this paper. It is composed of two majority gates that lead to low energy consumption and high hardware efficiency. The proposed compressor is utilized in the approximate partial product reduction of a modified 8×8 Dadda multiplier with a truncated structure. Experimental results show that this multiplier realizes a significant reduction in hardware cost, especially in terms of power and area, on average by up to 40% and 31% respectively, compared to exact and state-of-the-art designs. The application of image multiplication is also presented to assess the practicability of the multiplier. The results show that the proposed multiplier results in images with higher quality in peak signal to noise ratio (PSNR) and mean structural similarity index metric (MSSIM) compared to other designs.
List of the following materials will be included with the Downloaded Backup:Abstract:
Approximate addition is a technique to trade off energy consumption and output quality in error-tolerant applications. In prior art, bit truncation has been explored as a lever to dynamically trade off energy and quality. In this brief, an innovative bit truncation strategy is proposed to achieve more graceful quality degradation compared to state-of-the-art truncation schemes. This translates into energy reduction at a given quality target. When applied to a ripple-carry adder, the proposed bit truncation approach improves quality by up to 8.5 dB in terms of peak signal-to-noise ratio, compared to traditional bit truncation. As a case study, the proposed approach was applied to a discrete cosine transform engine. In comparison with prior art, the proposed approach reduces energy by 20%, at insignificant delay and silicon area overhead.
List of the following materials will be included with the Downloaded Backup:Abstract:
A scalable approximate multiplier, called truncation- and rounding-based scalable approximate multiplier (TOSAM) is presented, which reduces the number of partial products by truncating each of the input operands based on their leading one-bit position. In the proposed design, multiplication is performed by shift, add, and small fixed-width multiplication operations resulting in large improvements in the energy consumption and area occupation compared to those of the exact multiplier. To improve the total accuracy, input operands of the multiplication part are rounded to the nearest odd number. Because input operands are truncated based on their leading one-bit positions, the accuracy becomes weakly dependent on the width of the input operands and the multiplier becomes scalable. Higher improvements in design parameters (e.g., area and energy consumption) can be achieved as the input operand widths increase. To evaluate the efficiency of the proposed approximate multiplier, its design parameters are compared with those of an exact multiplier and some other recently proposed approximate multipliers. Results reveal that the proposed approximate multiplier with a mean absolute relative error in the range of 11%–0.3% improves delay, area, and energy consumption up to 41%, 90%, and 98%, respectively, compared to those of the exact multiplier. It also outperforms other approximate multipliers in terms of speed, area, and energy consumption. The proposed approximate multiplier has an almost Gaussian error distribution with a near-zero mean value. We exploit it in the structure of a JPEG encoder, sharpening, and classification applications. The results indicate that the quality degradation of the output is negligible. In addition, we suggest an accuracy configurable TOSAM where the energy consumption of the multiplication operation can be adjusted based on the minimum required accuracy.
List of the following materials will be included with the Downloaded Backup:Base Paper Abstract:
Approximate computing is a promising approach for reducing power consumption and design complexity in applications that accuracy is not a crucial factor. Approximate multipliers are commonly used in error-tolerant applications. This paper presents three approximate 4:2 compressors and two approximate multiplier designs, aiming at reducing the area and power consumption, while maintaining acceptable accuracy. The paper seeks to develop approximate compressors that align positive and negative approximations for input patterns that have the same probability. Additionally, the proposed compressors are utilized to construct approximate multipliers for different columns of partial products based on the input probabilities of the two compressors in adjacent columns. The proposed approximate multipliers are synthesized using the 28nm technology. Compared to the exact multiplier, the first proposed multiplier improves power × delay and area × power by 91% and 86%, respectively, while the second proposed multiplier improves the two parameters by 90% and 84%, respectively. The performance of the proposed approximate methods was assessed and compared with the existing methods for image multiplication, sharpening, smoothing and edge detection. Also, the performance of the proposed multipliers in the hardware implementation of the neural network was investigated, and the simulation results indicate that the proposed multipliers have appropriate accuracy in these applications.
List of the following materials will be included with the Downloaded Backup:We can provide Online Support Wordlwide, with proper execution, explanation and additionally provide explanation video file for execution and explanations.
NXFEE, will Provide on 24x7 Online Support, You can call or text at +91 9789443203, or email us nxfee.innovation@gmail.com
Customer are advice to watch the project video file output, and before the payment to test the requirement, correction will be applicable.
After payment, if any correction in the Project is accepted, but requirement changes is applicable with updated charges based upon the requirement.
After payment the student having doubts, correction, software error, hardware errors, coding doubts are accepted.
Online support will not be given more than 3 times.
On first time explanation we can provide completely with video file support, other 2 we can provide doubt clarifications only.
If any Issue on Software license / System Error we can support and rectify that within end of day.
Extra Charges For duplicate bill copy. Bill must be paid in full, No part payment will be accepted.
After payment, to must send the payment receipt to our email id.
Powered by NXFEE INNOVATION, Pondicherry.
Copyright © 2021 Nxfee Innovation.