Design and Implementation of an Ultra Efficient Approximate Multiplier With Error Compensation for Error-Resilient Applications

The objective of this work is to design and implement an ultra-efficient approximate multiplier that achieves significant reductions in area, power consumption, and delay while maintaining acceptable accuracy for error-resilient applications. The proposed approach aims to exploit application-level error tolerance by introducing controlled approximation in multiplication and incorporating an effective error compensation mechanism to minimize accuracy loss. The design is implemented and evaluated using hardware description languages and synthesized on FPGA/CMOS platforms, with performance assessed through standard error metrics and hardware parameters to demonstrate its suitability for applications such as image processing, signal processing, and AI accelerators.