Search papers, labs, and topics across Lattice.
This paper addresses the memory bandwidth and energy bottlenecks of von Neumann architectures for large-scale database search by leveraging hyperdimensional (HD) computing with in-situ computation on ferroelectric NAND (FE-NAND) memory. The researchers fabricated quad-level FE-NAND strings capable of in-situ multi-level cell dot product operations, enabling Hamming similarity calculations for HD computing. Experimental results and simulations demonstrate >90% search accuracy on TB-scale proteomics datasets, achieving significant speedup and energy efficiency improvements over existing solutions.
Massive database search sees 1,000x speedup and 10,000x energy efficiency gains by performing hyperdimensional computations directly within multi-bit ferroelectric NAND memory.
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.
