Vector Similarity Search (VSS) on edge devices is increasingly essential for data privacy but faces substantial latency and energy overhead due to frequent data transfers between storage and DRAM. To address these challenges, we propose the Intelligent Cognition Engine (ICE), a fully digital non-volatile in-memory computing (nvIMC) framework designed for integration with commercial 3DnandFlash. ICE avoids the use of analog-digital conversion (ADC/DAC) and reduces data movement by performing vector similarity computation directly withinnandstorage. Key architectural components include a digital page multiplier, a two’s-complement accumulator that supports signed computation with minimal circuit modification, and a hierarchical Top-N search strategy to reduce unnecessary data accesses. The proposed framework is evaluated through a combination of post-layout circuit simulations, representative silicon measurements, and system-level modeling on edge platforms. Experimental results indicate that ICE can achieve 17.8–$122.6\times $speedup and 11.5–$162\times $improvement in energy efficiency compared to conventional von Neumann-based approaches, demonstrating the feasibility and scalability of fully digital 3Dnand-based nvIMC for edge AI workloads.

Vector similarity search (VSS) for unstructured vectors generated via machine learning methods is a promising solution for many applications, such as face search. With increasing awareness and concern about data security requirements, there is a compelling need to store data and process VSS applications locally on edge devices rather than send data to servers for computation. However, the explosive amount of data movement from NAND storage to DRAM across memory hierarchy and data processing of the entire dataset consume enormous energy and require long latency for VSS applications. Specifically, edge devices with insufficient DRAM capacity will trigger data swap and deteriorate the execution performance. To overcome this crucial hurdle, we propose an intelligent cognition engine (ICE) with cognitive 3D NAND, featuring non-volatile in-memory computing (nvIMC) to accelerate the processing, suppress the data movement, and reduce data swap between the processor and storage. This cognitive 3D NAND features digital nvIMC techniques (i. e., ADClDAC-free approach), high-density 3D NAND, and compatibility with standard 3D NAND products with minor modifications. To facilitate parallel INT8/INT4 vector-vector multiplication (VVM) and mitigate the reliability issue of 3D NAND, we develop a bit-error-tolerance data encoding and a two’s complement-based digital accumulator. VVM can support similarity computations (e.g., cosine similarity and Euclidean distance), which are required to search “the most similar data” right where they are stored. In addition, the proposed solution can be realized on edge storage products, e.g., embedded Multi-Media Card (eMMC). The measured and simulated results on real 3D NAND chips show that ICE enhances the system execution time by $17\times to 95\times$ and energy efficiency by $11\times to 140\times$, compared to traditional von Neumann approaches using state-of-the-art edge systems with MobileFaceNet on CASIA-WebFace dataset. To the best of our knowledge, this work demonstrates the first 3D NAND-based digital nvIMC technique with measured silicon data.

The visual perception is of great significance for advanced driving assistance systems or autonomous driving vehicles to recognize the surrounding scenes. In the adaptation to the real environments for collision warnings, a sensor system should be efficient and has the strong ability to detect small objects. This paper presents a forward collision warning technique which incorporates the object detection and depth estimation networks. A deep convolutional neural network is constructed with transfer connection blocks for object detection and classification. It is capable of small object detection under the real-time processing requirement. For depth estimation, a monocular based disparity estimation network is adopted to the stereo vision framework. The epipolar constraint is applied to increase the prediction accuracy. In the experiments, the performance evaluation is carried out on public driving datasets. The comparison with the state-of-the-art networks has demonstrated the feasibility of the proposed technique.