While memory-augmented neural networks (MANNs) offer an effective solution for few-shot learning (FSL) by integrating deep neural networks with external memory, the capacity requirements and energy overhead of data movement become enormous due to the large number of support vectors in many-class FSL scenarios. Various in-memory search solutions have emerged to improve the energy efficiency of MANNs. NAND-based multi-bit content addressable memory (MCAM) is a promising option due to its high density and large capacity. Despite its potential, MCAM faces limitations such as a restricted number of word lines, limited quantization levels, and non-ideal effects like varying string currents and bottleneck effects, which lead to significant accuracy drops. To address these issues, we propose several innovative methods. First, the Multi-bit Thermometer Code (MTMC) leverages the extensive capacity of MCAM to enhance vector precision using cumulative encoding rules, thereby mitigating the bottleneck effect. Second, the Asymmetric Vector Similarity Search (AVSS) reduces the precision of the query vector while maintaining that of the support vectors, thereby minimizing the search iterations and improving efficiency in many-class scenarios. Finally, the Hardware-Aware Training (HAT) method optimizes controller training by modeling the hardware characteristics of MCAM, thus enhancing the reliability of the system. Our integrated framework reduces search iterations by up to 32×, and increases overall accuracy by 1.58% to 6.94%.

Read mapping is a critical bottleneck in genome sequence analysis, requiring costly approximate string matching to identify potential matches between reads and a reference genome. Pre-alignment filtering methods aim to mitigate this issue by filtering out unnecessary mapping locations, and implementing them with processing-in-memory (PIM) approaches offers potential benefits by offloading filtering from the computing unit. However, the sparse number of potential mapping locations for each read limits the utilization of PIM's parallel computing capabilities, thereby hindering the overlapping of filtering and sequence alignment to hide filtering latency overheads. In this paper, we propose a 3D NAND-based in-storage pre-alignment filtering approach. Leveraging the read depth property, we introduce a read-centric pre-alignment filtering method that enables parallel comparison of multiple reads. We co-design software and hardware for in-situ processing of read-centric pre-alignment filtering within the storage, capitalizing on 3D NAND Flash's approximate parallel search capability. When integrating with a representative read mapping accelerator, our design achieves an average 1.36x performance improvement with comparable energy consumption. Compared to the state-of-the-art (SOTA) PIM solution, our design results in 123.8x and 53.3x performance gain and energy efficiency improvement.

Vector similarity search (VSS) is crucial in many AI applications, such as few-shot learning (FSL) and approximate nearest-neighbor search (ANNS), but it demands significant memory capacity and incurs substantial energy costs for data transfers during large-scale comparisons. Various in-memory search technologies have been developed to improve energy efficiency, with NAND-based multi-bit content-addressable memory (MCAM) standing out as a promising solution for its high density and large capacity. MCAM can operate in exact-search (ES) mode, supporting only perfect matches with low energy cost, or in approximate-search (AS) mode, enabling flexible VSS. However, AS mode incurs significant energy waste when comparing queries with non-target stored vectors. To address this issue, we propose Hybrid-M, a 3D NAND-based in-memory VSS architecture that integrates both modes into a single hybrid matching process, using ES mode as a filter to reduce redundant searches for AS mode. We apply three techniques to optimize this integration: range encoding for multi-level cells (MLC) to enhance filtering, search voltage shifts to mitigate the impact on AS accuracy and reduce matching currents, and a filtering-aware training method to further improve reliability and energy efficiency. Results show that Hybrid-M achieves comparable accuracy while reducing energy consumption by 67% to 83% compared to MACM-based VSS using only AS mode, across various many-class FSL and ANNS workloads.

Genomic sequence analysis deciphers and interprets an organism’s DNA, offering crucial insights into personalized medicine, disease diagnosis, evolutionary biology, and agricultural biotechnology. While Next-Generation Sequencing (NGS) has revolutionized genomics by providing a fast and cost-effective method for generating genomic sequences, the computational complexity of aligning short reads back to a reference genome remains a significant bottleneck. The exact-match-based preseeding filter has emerged as an effective and general methodology to address this issue, capable of removing 70% to 80% of exact-matched genomic reads at the source and applicable to a wide range of alignment tools. However, the state-of-the-art exact-match filter architecture, GenStore, encounters performance limitations due to the need to load reference sequences from NAND flash memory to the controller page by page.In this work, we propose a novel Solid-State Drive (SSD) architecture that leverages computing-in-NAND-flash techniques to perform match detection directly within memory. By harnessing the two-dimensional input capability of 3D NAND flash memory and integrating group testing methods, our design enables comparisons across hundreds of pages in a single read cycle and supports simultaneous multi-query searches. Combined with a Bloom filter for in-NAND search, our architecture significantly reduces data movement by 48% to 96%, achieves a speedup of 1.60× to 4.99× over GenStore, and delivers 30% higher energy efficiency with only a 4.5% circuit overhead.