Transformers have made remarkable contributions to natural language processing (NLP) and many other fields. Recently, transformer-based models have achieved state-of-the-art (SOTA) performance on computer vision tasks compared with traditional convolutional neural networks (CNNs). Unfortunately, existing CNN accelerators cannot efficiently support transformer due to the high computational overhead and redundant data accesses associated with the ‘KQV’ matrix operations in the transformer models. If the recently-developed NLP transformer accelerators are applied to the vision transformer (ViT) models, their efficiency would decrease due to three challenges. 1) Redundant data storage and access still exist in ViT data flow scheduling. 2) For matrix transposition in transformer models, the previous transpose-operation schemes lack flexibility, resulting in extra area overhead. 3) The sparse acceleration schemes for NLP in prior transformer accelerators cannot efficiently accelerate ViT with relatively fewer tokens. To overcome these challenges, we propose $P^{3}$ ViT, a computing-in-memory (CIM)-based architecture, to efficiently accelerate ViT, achieving high utilization on data flow scheduling. There are three key contributions: 1) P $^{3}$ ViT architecture supports three ping-pong pipeline scheduling modes, involving inter-core parallel and intra-core ping-pong pipeline mode (IEP-IAP $^{3})$ , inter-core pipeline and parallel mode (IEP $^{2})$ , and full parallel mode, to eliminate redundant memory accesses. 2) A two-way ping-pong CIM macro is proposed, which can be configured to regular calculation mode and transpose calculation mode to adapt to both Q $\times$ K $^{\mathrm{T}}$ and A $\times$ V tasks. 3) P $^{3}$ ViT also runs a small prediction network. It prunes redundant tokens to be a standard number hierarchically and dynamically, enabling high-throughput and high-utilization attention computation. Measurements show that P $^{3}$ ViT achieves 1.13 $\times$ higher energy efficiency than the state-of-the-art transformer accelerator and achieves 30.8 $\times$ and 14.6 $\times$ speedup compared to CPU and GPU.

Radio frequency identification technology (RFID) has empowered a wide variety of automation industries. Aiming at the current light-weight RFID encryption scheme with limited information protection methods, combined with the physical unclonable function (PUF) composed of resistive random access memory (RRAM), a new type of high-efficiency reconfigurable strong PUF circuit structure is proposed in this paper. Experimental results show that the proposed PUF shows an almost ideal value (50%) of inter-chip hamming distance (HD) (µ/$\sigma$ = 0.5001/0.0340) among 1000 PUF keys, and intra-chip HD results are very close to the ideal value (0). The bit error rate (BER) is as low as $3.8\times {10}^{-6}$ across one million challenges. Based on the RRAM PUF, we propose and implement a light weight RFID authentication protocol. By virtue of RRAM’s model ability, the protocol replaces the One-way Hash Function with a response chain mutual encryption algorithm. The results of test and analysis show that the protocol can effectively resist multiple threats such as physical attacks, replay attacks, tracking attacks and asynchronous attacks, and has good stability. At the same time, based on RRAM’s unique resistance variability, PUF also has the advantage of being reconfigurable, providing good security for RFID tags.

Radio frequency identification technology (RFID) has empowered a wide variety of automation industries, such as logistics and freight transportation. To further promote RFID tags adoption, security, power consumption, and cost have always been issues of general concern. This article presents the first synergy of the RFID tag with embedded resistive RAM (RRAM) array and RRAM-based reconfigurable strong physical unclonable function (R-SPUF). The RRAM not only meets the mass storage and technology downscaling but also renders the ultralow-cost “1-cent RFID tag” more feasible. Moreover, the R-SPUF facilitates multiple initializations until a satisfactory distribution and has strong secure keys benefiting from its reconfigurability that improves both safety and reliability. The complete system operates at 13.56 MHz and is compliant with the ISO14443-A and ISO10373-6 (test) protocols. The RFID tag was fabricated on a 1.1-mm2 die based on the 0.18- $\mu \text{m}$ CMOS process. Without resorting to the charge pumps for RRAM read–write operations, the total power consumption is as low as 52.3 $\mu \text{W}$ , of which the RRAM dissipates $16.2~\mu \text{W}$ under a wireless power supply.

This brief presents an analog front-end (AFE) for the detection of electroencephalogram (EEG) signals. The AFE is composed of four sections, chopper-stabilized amplifiers, ripple suppression circuit, RRAM-based lowpass FIR filter, and 8-bit SAR ADC. This is the first time that an RRAM-based lowpass FIR filter has been introduced in an EEG AFE, where the bio-plausible characteristics of RRAM are utilized to analyze signals in the analog domain with high efficiency. The preamp uses the symmetrical OTA structure, reducing power consumption while meeting gain requirements. The ripple suppression circuit greatly improves noise characteristics and offset voltage. The RRAM-based low-pass filter achieves a 40 Hz cutoff frequency, which is suitable for the analysis of EEG signals. The SAR ADC adopts a segmented capacitor structure, effectively reducing the capacitor switching power consumption. The chip prototype is designed in 40 nm CMOS technology. The overall power consumption is approximately 13 µW, achieving ultra-low-power operation.

Three-dimensional vertical resistive random access memory (VRRAM) is proposed as a promising candidate for increasing resistive memory storage density, but the performance evaluation mechanism of 3-D VRRAM arrays is still not mature enough. The previous approach to evaluating the performance of 3-D VRRAM was based on the write and read margin. However, the leakage current (LC) of the 3-D VRRAM array is a concern as well. Excess leakage currents not only reduce the read/write tolerance and liability of the memory cell but also increase the power consumption of the entire array. In this article, a 3-D circuit HSPICE simulation is used to analyze the impact of the array size and operation voltage on the leakage current in the 3-D VRRAM architecture. The simulation results show that rapidly increasing leakage currents significantly affect the size of 3-D layers. A high read voltage is profitable for enhancing the read margin. However, the leakage current also increases. Alleviating this conflict requires a trade-off when setting the input voltage. A method to improve the array read/write efficiency is proposed by analyzing the influence of the multi-bit operations on the overall leakage current. Finally, this paper explores different methods to reduce the leakage current in the 3-D VRRAM array. The leakage current model proposed in this paper provides an efficient performance prediction solution for the initial design of 3-D VRRAM arrays.

Super-resolution (SR) task using the convolutional neural network is a crucial task in improving image and video quality. The introduction of the residual block (RB) raises the depth of the algorithm to perform better reconstruction. The processing of the RB leads to a decrease in hardware utilization and frequent off-chip communications. It is hard to apply such algorithms on edge devices with limited performance. Computing-in-memory (CiM) is one promising method to reduce high power caused by massive data movement in multiply-accumulation computation. The algebraic sparsity (AS) is the structured sparsity (SS) optimization for imaging computing. However, it is an unsolved problem to simultaneously realize the texture sparsity (TS) of the image and the SS of the algorithm in the CiM scheme while maintaining high hardware utilization. Thus, we propose a CiM-based SR task accelerator. There are three key contributions: first, a texture-aware workflow and a dynamic grouping CiM engine can concurrently support TS coupling with AS. Second, a macro-level pipeline scheme together with two custom-sized CiM macros and a high reuse-rate Hadamard transformation circuit reaches 91% hardware utilization. Third, a novel weight update strategy is devised to reduce the performance loss induced by the weight updating. The accelerator prototype is fabricated in a 28-nm CMOS. It scores a 22.8-44.3-TOPS/W peak energy efficiency at the voltage supply of 0.54-1.1 V and the operating frequency of 50-200 MHz, indicating 1.8-6.8x higher compared to the state-of-the-art CiM processors.