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.

In traditional direct marketing, the implicit assumption is that customers will only purchase the product if they are contacted. In real business environments, however, there are "voluntary buyers, " who will still make the purchase in the absence of a contact. While no direct promotion is needed for voluntary buyers, the traditional response-driven paradigm tends to target such customers. This paper presents "influential marketing, " targeting only those whose purchase decisions can be positively influenced, i.e. buyers who are non-voluntary. Our novel, practical solution to this problem gives promising results.

This paper proposes the design of a current-mode resonator with complex coefficients based on fully-balanced operational transconductance amplifiers (FBOTAs) and grounded capacitors. The proposed current-mode resonator can be used in a quadrature /spl Delta//spl Sigma/ modulation for digital radio or can be employed as a foundation to realize high-order complex filters with in cascade or leapfrog for wireless local area network (LAN) applications. For realizing the current-mode resonator herein, only two grounded capacitors as passive elements are required. Thus, the proposed current-mode resonator is suitable for the integrated circuit realization. Chip design and measured results are presented to verify the theoretical analysis.