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@article{197707,
        author = {Dr. Kavita Jain},
        title = {Arithmetic Efficiency Algorithms for Artificial Intelligence: A Survey of Vedic Computational Methods and Their Complexity-Theoretic Foundations},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {6336-6340},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=197707},
        abstract = {The arithmetic cost of AI computation — dominated by multiply-accumulate (MAC) operations in neural network training, inference, and signal processing — represents a first-order engineering constraint that conventional silicon scaling cannot resolve. This survey provides a cross-domain complexity map of Vedic mathematical algorithms against modern AI architectures, formally distinguishing proven algorithmic gains from empirically observed ones. Analysed across five domains — VLSI design, neural network acceleration, digital signal processing, natural language processing, and quantum computing — the reviewed literature documents 32.7% mean VLSI power reduction, 29% faster neural network convergence, and 23% qubit reduction in NISQ-era quantum circuits. Three formally stated open research problems are identified, each constituting an actionable contribution to algorithm theory or AI systems design. The survey is intended as a theoretically grounded reference for researchers evaluating Vedic arithmetic primitives as candidate efficiency pathways in hardware-constrained and embedded AI systems.},
        keywords = {Vedic mathematics; arithmetic algorithms; computational complexity; VLSI design; neural network acceleration; embedded systems; quantum computing.},
        month = {April},
        }