Reconfigurable Multiplier Architectures for Neuromorphic AI: Simulation and Performance Evaluation

The new area of neuromorphic computing inspired by biological neural systems is proving to be a cornerstone for energy-efficient spiking neural networks. Fixed-precision multipliers have dominated current hardware implementations of SNNs, with high power consumption and significant area usage in FPGA deployments, and it does not adapt well to the varied demands for precision in neuromorphic workloads. This paper proposes a novel Reconfigurable Multiplier (ReM) architecture that dynamically adapts precision based on workload requirements, leveraging approximate computing techniques to realize significant reductions in power consumption and resource usage. The design was simulated and analyzed using Xilinx Vivado with metrics such as power, area, and timing evaluated. Results demonstrate that the architecture is able to meet the neuromorphic systems energy and area constraints while providing acceptable levels of computational accuracy. This work therefore demonstrates the potential of using reconfigurable arithmetic units for high efficiency power neural processors in the future.