A Reversible-Logic Based Architecture for Artificial Neural Network
Objective
The main objective of this paper is to reduce the power in ANN by using the reversible logic design. The proposed architecture has been realized using a reversible multiplier followed by a reversible adder
Abstract
In this project, a novel design of Artificial Neural Network (ANN) using reversible logic gates is implemented. ANN can be described as (i) Scalar weight matrix multiplication with input scalars, (ii) Sum of weighted input scalars, (iii) Scalar-to-scalar non-linearity calculation. High power consumption in ANN, is a crucial issue that can cause over reliability degradation, chip damage.
Hence the weighted sums and multiplication are performed with reversible logic for better power reduction. These modules have been implemented using Verilog HDL and the results are obtained using Xilinx ISE 14.7.
Keywords: Artificial neural network, Reversible logic, Multiplier, Low power circuits
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Specifications
Software Requirements:
- Xilinx ISE Tool/Xilinx Vivado
- HDL: Verilog
Hardware Requirements:
- Microsoft® Windows XP
- Intel® Pentium® 4 processor or Pentium 4 equivalent with SSE support
- 512 MB RAM
- 100 MB of available disk space
Learning Outcomes
- Basics of Digital Electronics
- FPGA design Flow
- Introduction to Verilog Coding
- Different modeling styles in Verilog
- Data Flow modeling
- Structural modeling
- Behavioral modeling
- Mixed level modeling
- Introduction to Arithmetic circuits
- About reversible logic
- Knowledge on Neural network
- Different reversible gates
- Knowledge on multiplier designs using reversible gates
- About Sigmoid function
- Applications in real time
- Xilinx ISE 14.7/Xilinx Vivado for design and simulation
- Generation of Netlist
- Solution providing for real time problems
- Project Development Skills:
- Problem Analysis Skills
- Problem Solving Skills
- Logical Skills
- Designing Skills
- Testing Skills
- Debugging Skills
- Presentation Skills
- Thesis Writing Skills
Paper Publishing
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Disclaimer - Takeoff Edu Group Projects are not associated or affiliated with IEEE in any way. The IEEE Projects mentioned here are mentioned in the context of student projects, whose ideas are derived from IEEE publications, not projects of or by IEEE.
