Design and Implementation of 32 Bit Adder Using Neuromorphic Computing

Abstract

Neuromorphic computing is a brain-inspired approach to computation that seeks to mimic the neural structure and functions of the human brain in electrical devices. Unlike traditional Von Neumann architectures, which divide memory and processing units, neuromorphic systems integrate these tasks more intimately, similar to how biological neurons and synapses interact. Spiking neural networks (SNNs) are at the heart of neuromorphic computing. Information is conveyed through discrete electrical pulses or \"spikes,\" much how neurons communicate in the brain.These systems operate in an event-driven manner,which means that computations take place only when necessary, resulting in significant power savings over traditional digital systems.To explore the application of neuromorphic computing concepts to the development and simulation of a 32-bit adder in Xilinx Vivado. The architecture uses event-driven computation and spike-based temporal encoding to accomplish arithmetic addition by utilizing the ideas of spiking neural networks (SNNs). A rate-based encoding approach is used to encode input operands into spike trains, which are subsequently processed by models of spiking neurons that replicate the actions of biological membrane potentials and synapses. Spike interactions and temporal dynamics are used to achieve summation and carry propagation in the adder logic, which is built utilizing a series of half-adders described via spiking behavior.

Introduction

Modern computing demands—such as low power, high speed, and efficient parallel processing—challenge conventional digital architectures, especially in edge computing and AI. Neuromorphic computing, inspired by the human brain's efficient and parallel neural processing, offers a promising alternative. Originating in the 1980s, neuromorphic systems emulate biological neurons using spiking neural networks (SNNs) that process information via spike timing, integrating memory and computation in an event-driven, distributed manner.

This work presents the design and simulation of a 32-bit adder based on neuromorphic principles. Inputs are encoded into spike trains representing temporal patterns processed by spiking neurons modeled on integrate-and-fire dynamics. The neuron membrane potentials accumulate spikes and generate outputs when thresholds are exceeded. The system uses time-step control and spike decoding to produce a binary sum output equivalent to conventional addition.

Simulation results using Verilog and Xilinx Vivado demonstrate accurate addition (e.g., 20 + 10 = 30, or 0x12345678 + 0x87654321 = 0x99999999) validating the architecture's correctness. This neuromorphic approach offers advantages in parallelism, energy efficiency, fault tolerance, and biological fidelity, potentially paving the way for scalable, low-power arithmetic circuits and next-generation AI hardware.

Conclusion

This study presents a practical approach to abiologically inspired solution for performing 32-bit addition through neuromorphic computing techniques. By encoding binary data into temporal spike trains and applying spiking neuron dynamics, the proposed model successfully simulates arithmetic operations using event-driven logic. Threshold-based neuron firing and temporal integration allow the system to mimic addition with precision and computational efficiency. The Verilog-based implementation was verified using Xilinx Vivado simulation tools, confirming functional correctness and stability across a wide range of input patterns. The use of Vivado enables in-depth analysis of timing behavior and resource utilization in a software simulation environment, providing a reliable platform for validating neuromorphic designs. Overall, this approach demonstrates the potential of integrating spiking neural models into arithmetic operations, offering a novel path toward efficient, low-power, and scalable software-defined computation.

References

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Copyright

Copyright © 2025 Sushmitha N, Dr. M C Chandrashekhar . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.