Multi-bit Ferroelectric-NAND for High-throughput Massive Database Search
The growing demand for large-scale database search in data-intensive applications, ranging from proteomics to autonomous systems, has exposed fundamental limitations in von Neumann architectures due to memory bandwidth and energy constraints. Hyperdimensional (HD) computing offers a robust and parallelizable framework for such tasks, but its practical implementation remains challenged by high memory demands. Ultra-high-density, energy-efficient ferroelectric NAND (FE-NAND) memory provides a potential solution by enabling in-situ computation. We fabricate quad-level FE-NAND strings with wide memory windows, disturb resilience, and robust retention. Using these planar FE-NAND strings as building blocks, we experimentally demonstrate in-situ multi-level cell (MLC) dot product operations at the single-cell level and, using experimentally calibrated physics-based simulations, demonstrate Hamming similarity calculations between reference and query hypervectors (HV). This platform leverages the inherent error tolerance of HD computing to achieve >90% search accuracy even at high logic levels (TLC, QLC). When benchmarked on Open Modification Search (OMS) tasks in proteomics with TB-scale datasets, our system shows nearly 1,000x speedup and over 10,000x energy efficiency improvement compared to incumbent solutions. These results establish FE-NAND as a viable in-storage compute architecture for large-scale, high-dimensional data processing.