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sale OFFER 38%
Multiple Constant Multiplication at Minimal Hardware Cost
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Toward the Multiple Constant Multiplication at Minimal Hardware Cost

Original price was: ₹16,000.00.Current price is: ₹10,000.00.
Source : Verilog HDL

Base Paper Abstract:

Multiple Constant Multiplication (MCM) over integers is a frequent operation arising in embedded systems that require highly optimized hardware. An efficient way is to replace costly generic multiplication by bit-shifts and additions, i. e. a multiplier less circuit. In this work, we improve the state of-the-art optimal approach for MCM, based on Integer Linear Programming (ILP). We introduce a new low-level hardware cost metric, which counts the number of one-bit adders and demonstrate that it is strongly correlated with the LUT count. This new model permitted us to consider intermediate truncations that permit to significantly save resources when a full output precision is not required. We incorporate the error propagation rules into our ILP model to guarantee a user-given error bound on the MCM results. The proposed ILP models for multiple flavors of MCM are implemented as an open-source tool and, combined with an automatic code generator, provide a complete coefficient-to-VHDL flow. We evaluate our models in extensive experiments, and propose an in-depth analysis of the impact that design metrics have on synthesized hardware.

List of the following  materials will be included with the Downloaded Backup:
1. Source code ( Modelsim/ Xilinx/ Quartus/ DSCH3/ Microwind)
2. Existing and Proposed Project Comparison with output video 
3. Basic Documentation (20 to 30 Pages):
3.1 Proposed Title
3.2 Proposed Abstract
3.3 Advantages & Disadvantages
3.4 Improvement of this Project
3.5 Existing System with Notes
3.6 Proposed System with Notes
3.7 Literature Survey
3.8 Software Related Notes
3.9 VLSI and HDL Language / Tanner Notes
3.10 References & Reference Paper for More Pages
4. Online Support ( Any Desk / Zoom / Google Meet)
 
sale OFFER 33%
modular_adder
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Towards Efficient Modular Adders based on Reversible Circuits

Original price was: ₹18,000.00.Current price is: ₹12,000.00.
Source : VHDL

Abstract:

Reversible logic is a computing paradigm that has attracted significant attention in recent years due to its properties that lead to ultra-low power and reliable circuits. Reversible circuits are fundamental, for example, for quantum computing. Since addition is a fundamental operation, designing efficient adders is a cornerstone in the research of reversible circuits. Residue Number Systems (RNS) has been as a powerful tool to provide parallel and fault-tolerant implementations of computations where additions and multiplications are dominant. In this paper, for the first time in the literature, we propose the combination of RNS and reversible logic. The parallelism of RNS is leveraged to increase the performance of reversible computational circuits. Being the most fundamental part in any RNS, in this work we propose the implementation of modular adders, namely modulo 2n-1 adders, using reversible logic. Analysis and comparison with traditional logic show that modulo adders can be designed using reversible gates with minimum overhead in comparison to regular reversible adders.

List of the following  materials will be included with the Downloaded Backup:
1. Source code ( Modelsim/ Xilinx/ Quartus/ DSCH3/ Microwind)
2. Existing and Proposed Project Comparison
3. Architecture Diagram
4. Algorithm with Flow chart
5. Report for Phase1 and Phase2
6. Proposed abstract document
7. Reference materials
8. Literature survey with Reference Document
9. Online Support ( Team viewer/ Ammy Admin)
sale OFFER 40%
EEG_Approximate_Computing
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Two Efficient Approximate Unsigned Multipliers by Developing New Configuration for Approximate 4:2 Compressors

Original price was: ₹20,000.00.Current price is: ₹12,000.00.
Source : Verilog HDL

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:
1. Source code ( Modelsim/ Xilinx/ Quartus/ DSCH3/ Microwind)
2. Existing and Proposed Project Comparison with output video 
3. Basic Documentation (20 to 30 Pages):
3.1 Proposed Title
3.2 Proposed Abstract
3.3 Advantages & Disadvantages
3.4 Improvement of this Project
3.5 Existing System with Notes
3.6 Proposed System with Notes
3.7 Literature Survey
3.8 Software Related Notes
3.9 VLSI and HDL Language / Tanner Notes
3.10 References & Reference Paper for More Pages
4. Online Support ( Any Desk / Zoom / Google Meet)
 
sale OFFER 38%
Variable-Precision Approximate Floating-Point
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Variable-Precision Approximate Floating-Point Multiplier for Efficient Deep Learning Computation

Original price was: ₹16,000.00.Current price is: ₹10,000.00.
Source : Verilog HDL

Base Paper Abstract:

In this brief, a variable-precision approximate floating-point multiplier is proposed for energy efficient deep learning computation. The proposed architecture supports approximate multiplication with BFloat16 format. As the input and output activations of deep learning models usually follow normal distribution, inspired by the posit format, for numbers with different values, different precisions can be applied to represent them. In the proposed architecture, posit encoding is used to change the level of approximation, and the precision of the computation is controlled by the value of product exponent. For large exponent, smaller precision multiplication is applied to mantissa and for small exponent, higher precision computation is applied. Truncation is used as approximate method in the proposed design while the number of bit positions to be truncated is controlled by the values of the product exponent. The proposed design can achieve 19% area reduction and 42% power reduction compared to the normal BFloat16 multiplier. When applying the proposed multiplier in deep learning computation, almost the same accuracy as that of normal BFloat16 multiplier can be achieved.

List of the following  materials will be included with the Downloaded Backup:
1. Source code ( Modelsim/ Xilinx/ Quartus/ DSCH3/ Microwind)
2. Existing and Proposed Project Comparison
3. Architecture Diagram
4. Algorithm with Flow chart
5. Report for Phase1 and Phase2
6. Proposed abstract document
7. Reference materials
8. Literature survey with Reference Document
9. Online Support ( Team viewer/ Ammy Admin)
               

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