Design and Implementation of 32 Bit Adder Using Neuromorphic Computing

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.