Recently, memory-augmented neural networks (MANNs) have gained significant attention as a critical solution for few-shot learning (FSL). These networks leverage external memory to store prior knowledge, thereby enhancing classification efficiency. Spin-transfer torque magnetic random access memory (STT-MRAM) is particularly suited for this application due to its compact cell size, excellent data retention, and scalability. In this article, we introduce a STT-MRAM-based near-memory computing (NMC) macro specifically designed for MANNs. Our approach incorporates several key innovations aimed at overcoming challenges in hardware implementation while improving MANN performance as follows: 1) a parallel computing architecture within the NMC to expedite$L1$distance computations; 2) a memory invert coding (MIC) and self-termination write (STW) scheme that reduce write operations and energy consumption, addressing the issues of frequent writes and high write currents during the training phase of MANNs; 3) a dynamic offset-compensation sense amplifier (DOC-SA) and high-throughput switch-capacitor (HTSC) readout scheme to improve read accuracy and throughput, tackling low read margins and limited readout bandwidth; 4) an exploration of MANN architectures validates the reusability of the NMC macro. The optimized matching-networks (MCHnets)-based structure achieves an accuracy exceeding 90% in five-way and eight-way Omniglot classification tasks. Fabricated with a 40-nm CMOS technology, our design achieves classification accuracies of 96.37% for eight-way-five-shot tasks and 93.72% for 16-way-five-shot tasks on the Omniglot dataset utilizing the optimized MCHnet, showcasing an impressive energy efficiency of 6.47 TOPS/W at the basis of 16-bit$L1$distance computing in the classification tasks of MANN.

Memory-augmented neural network (MANN) has gained attention as a pivotal solution for few-shot learning (FSL). Among the candidates for associative memory in MANN accelerators, spin-transfer torque magnetic random-access memory (STT-MRAM) stands out for its compact cell area, long data retention time, and excellent scalability. In this paper, we propose an STT-MRAM near-memory computing (NMC) macro for MANN acceleration. The macro contains following innovations: 1) An array-level parallel computing architecture for L1 distance calculation. 2) A low-area-overhead memory-invert coding technique to reduce write energy consumption. 3) A configurable dynamic offset-compensation sense amplifier (CDOC-SA) to improve classification accuracy. Fabricated in 40nm CMOS process, our macro demonstrates an energy efficiency of 6.47 TOPS/W, achieving the classification accuracy of 98.3% and 93% for 8-way-5-shot tasks and 16-way-5-shot tasks on the Omniglot dataset.

Non-volatile Compute-in-Memory (CIM), especially high-speed MRAM CIM, promises to be a solution of “Memory Wall” problem in power-sensitive artificial intelligence edge devices. However, the low resistance and low on/off ratio limit the row parallelism and efficiency of MRAM CIM macros. To overcome these challenges, this work proposes the following: 1) a series 3T1MTJ bit-cell CIM architecture; 2) an input-aware and self-generated dynamic reference array; 3) a high-speed readout pipeline circuit. The proposed macro eliminates errors of high Row-Parallel multiply-and-accumulate (MAC) operation with 150 TOPS/W peak energy efficiency simulated using 40nm process and STT-MTJ.

Physically Unclonable Functions (PUFs) are emerging security primitives for authentication due to its high physical security. Especially for those with excellent area-efficiency and reliable immunity against attacks, the demand is larger. In order to achieve higher security and area-efficiency, this paper proposes a strong PUF based on resistive random-access memory (RRAM). We exploit both the switching randomness and intrinsic resistance distribution of RRAM to increase the randomness of entropy source, and design a novel strong PUF structure with double-read process to increase the challenge-response pairs (CRPs) and area-efficiency. A double XOR process is proposed to enhance the immunity against machine learning attack (MLA) with low area-overhead. Compared with the state of the art, the number of CRP has been greatly improved, demonstrating a better area-utilization. Simulation results show that the CRP generation time is 1. 8us, mean intra-HD of1.93%, inter-HD of 49.95% and uniformity of 49.02%. The above features of the proposed strong PUF make it a promising candidate for Internet of Things (IoTs) authentication applications.