Number Theoretic Transform (NTT) is an essential mathematical tool for computing polynomial multiplication in promising lattice-based cryptography. However, costly division operations and complex data dependencies make efficient and flexible hardware design to be challenging, especially on resource-constrained edge devices. Existing approaches either focus on only limited parameter settings or impose substantial hardware overhead. In this paper, we introduce a hardware-algorithm methodology to efficiently accelerate NTT in various settings using in-cache computing. By leveraging an optimized bit-parallel modular multiplication and introducing costless shift operations, our proposed solution provides up to 29x higher throughput-per-area and 2.8-100x better throughput-per-area-per-joule compared to the state-of-the-art.