Accelerating \(x^n\) Computation Using a Hardware-Integrated custom POW Instruction in 64-bit RISC-V Systems for Applied Mathematical Workloads

Exponentiation is a critical computational operation used across domains such as scientific computation, cryptography, biomedical signal processing, climate modeling, and embedded AI. In software environments, repeated evaluation of \(x^n\) often incurs significant execution latency and energy costs. To mitigate these challenges, this paper introduces a custom POW instruction implemented in hardware within a 64-bit RISC-V pipeline. The POW instruction has been integrated into the MARSS-RISCV cycle-accurate simulator, involving substantial modifications to the GNU toolchain, including the instruction decoder, simulator pipeline, and assembler/disassembler. Performance evaluations of this custom instruction were conducted against conventional for-loop constructs and math library exponentiation methods, utilizing a comprehensive benchmark suite. Experimental results indicate that the POW instruction achieves an average execution time of 21.7 ms, yielding speedups of 178 times over loop-based implementations and 147 times compared to math-library methods. Cycle counts were reduced by over 7 times and 8 times, respectively, with further improvements in Instructions Per Cycle (IPC) confirming enhanced pipeline efficiency and lower execution overhead. These findings highlight the efficacy of custom instruction design in accelerating compute-intensive tasks like exponentiation, emphasizing the advantages of RISC-V ISA extensibility for high-performance, energy-efficient, and tailored embedded computing solutions.