₹25,000.00Original price was: ₹25,000.00.₹8,000.00Current price is: ₹8,000.00.
Source : Verilog HDLCustomized Bit Size Available8-Bit = Rs. 8,000/- Only Multiplier8-Bit = Rs. 12,000/- with Image Multiplication16-Bit = Rs. 14,000/- with Image Multiplication ( 8bit + 16bit)32-Bit = Rs. 20,000/- with Image Multiplication ( 8bit + 16bit + 32bit)
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.
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₹15,000.00Original price was: ₹15,000.00.₹10,000.00Current price is: ₹10,000.00.
Source Code : VHDLAbstract:
Inexact computing is particularly interesting for computer arithmetic designs. Implementation of 8X8 truncated multipliers using Very High Speed Integrated Circuit Hardware Description Language (VHDL). Truncated multipliers can be used in the image multiplication application. This multiplier is automatically truncating the output and reduces the power consumption and are comparing to other multipliers. The proposed architecture of this paper analysis the logic size, area and power consumption using Xilinx 14.2.
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Approximate circuits have been considered for applications that can tolerate some loss of accuracy with improved performance and/or energy efficiency. Multipliers are key arithmetic circuits in many of these applications including digital signal processing (DSP). In this paper, a novel approximate multiplier with a low power consumption and a short critical path is proposed for high-performance DSP applications. This multiplier leverages a newly designed approximate adder that limits its carry propagation to the nearest neighbors for fast partial product accumulation. Different levels of accuracy can be achieved by using either OR gates or the proposed approximate adder in a configurable error recovery. The multipliers using these two error reduction strategies are referred to as approximate multiplier 1 (AM1) and approximate multiplier 2 (AM2), respectively. Both AM1 and AM2 have a low mean error distance, i.e., most of the errors are not significant in magnitude. Compared to a Wallace multiplier optimized for speed, an 8×8 AM1 with 4 MSBs (most significant bits) for error reduction and synthesized using a 28 nm CMOS process shows a 60% reduction in delay (when optimized for delay) and a 42% reduction in power dissipation (when optimized for area). In a 16×16 design, half of the least significant partial products are truncated for AM1 and AM2, which are thus denoted as TAM1 and TAM2, respectively. Compared with the Wallace multiplier, TAM1 and TAM2 save from 50% to 66% in power, when optimized for area. Compared to existing approximate multipliers, AM1, AM2, TAM1 and TAM2 show significant advantages in accuracy with a high performance. AM2 has a better accuracy compared to AM1 but with a longer delay and higher power consumption. Image processing applications including image sharpening and smoothing are considered to show the quality of the approximate multipliers in error-tolerant applications. By utilizing an appropriate error recovery, the proposed approximate multipliers achieve similar processing accuracy as traditional exact multipliers, but with significant improvements in power.
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