Power-Efficient 8-Bit ALU Design Using Squirrel Search and Swarm Intelligence Algorithms
Abstract
The Arithmetic Logic Unit (ALU) serves as a core digital computing element which performs arithmetic functions along with logic operations. The normal operation of ALU designs leads to increased power consumption because of signal redundancy and continuous operation when new data inputs are unavailable. The research implements the Squirrel Search Algorithm (SSA) combined with Swarm Intelligence Algorithm (SIA) for 8-bit ALU optimization to achieve maximum resource efficiency alongside computational accuracy. The optimization properties of SSA and SIA make them ideal choices for digital circuit design applications because they yielded successful results in power-aware systems. The proposed method utilizes SSA-based conditional execution paired with SIA-based transition minimization to direct operations to execute only during fluctuating input data conditions thus eliminating undesired calculations. Studies confirm SSA and SIA function more effectively than distributed clock gating for power saving because they enable runtime-dependent optimization without creating significant computational overhead. The experimental Xilinx Vivado tests executed on an AMD Spartan-7 FPGA (XC7S50FGGA484) running at 100 MHz frequency established that SSA eliminates power consumption from 6 mW to 2 mW, and SIA achieves a power level of 4 mW. The SSA algorithm generates worst negative slack (WNS) values of 8.740 ns which SIA produces as 6.531 ns improving system timing performance. SSA-optimized ALU requires the same number of LUTs as the unoptimized design at 42 LUTs yet SIA uses 50 LUTs because of added logical elements. We observe no changes in flip-flop use during SSA where nine FFs remain yet SIA shows an increase in its usage up to 29 FFs due to input tracking. The study proves that bio-inspired methods create energy-efficient platforms which make them ideal for implementing ALU designs with FPGAs. Research studies demonstrate that hybrid swarm intelligence techniques represent an unexplored potential to optimize power-efficient architectures thus reinforcing their significance for future high-performance energy-efficient digital systems.
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