Probabilistic Compute-in-Memory Design For Efficient Markov Chain Monte Carlo Sampling

Markov chain Monte Carlo (MCMC) is a widely used sampling method in modern artificial intelligence and probabilistic computing systems. It involves repetitive random number generations and thus often dominates the latency of probabilistic model computing. Hence, we propose a compute-in-memory (CIM) based MCMC design as a hardware acceleration solution. This work investigates SRAM bitcell stochasticity and proposes a novel ``pseudo-read'' operation, based on which we offer a block-wise random number generation circuit scheme for fast random number generation. Moreover, this work proposes a novel multi-stage exclusive-OR gate (MSXOR) design method to generate strictly uniformly distributed random numbers. The probability error deviating from a uniform distribution is suppressed under $10^{-5}$. Also, this work presents a novel in-memory copy circuit scheme to realize data copy inside a CIM sub-array, significantly reducing the use of R/W circuits for power saving. Evaluated in a commercial 28-nm process development kit, this CIM-based MCMC design generates 4-bit$\sim$32-bit samples with an energy efficiency of $0.53$~pJ/sample and high throughput of up to $166.7$M~samples/s. Compared to conventional processors, the overall energy efficiency improves $5.41\times10^{11}$ to $2.33\times10^{12}$ times

MXene Reinforced PAA/PEDOT:PSS/MXene Conductive Hydrogel for Highly Sensitive Strain Sensors

Abstract Conductive hydrogel has a vital application prospect in flexible electronic fields such as electronic skin and force sensors. Developing conductive hydrogel with significant toughness and high sensitivity is urgently needed for application research. In this work, a strong and sensitive strain sensor based on conductive hydrogel is demonstrated by introducing MXene (Ti3C2Tx) into the micelle crosslinked polyacrylic acid (PAA)/poly(3,4‐ethylenedioxythiophene):poly(styrene‐sulfonate) (PEDOT:PSS) hydrogel network. The functional polymer micelle crosslinkers can dissipate external stress by deformation, endowing the hydrogel with high strength. The combination of MXene both improves the polymer network structure and the conductive pathways, further enhancing the mechanical properties and sensing performance. Resultantly, the flexible strain sensor base on PAA/PEDOT:PSS/MXene conductive hydrogel exhibits excellent sensing performance with a high gauge factor of 20.86, a large strain detection range of 1000%, as well as good adhesion on different interfaces. Thus, it can be used to monitor various movements of the human body and identify all kinds of handwriting, showing great potential into wearable electronics

Integrated Memristor Network for Physiological Signal Processing

Abstract Humans are complex organisms made by millions of physiological systems. Therefore, physiological activities can represent physical or mental states of the human body. Physiological signal processing is essential in monitoring human physiological features. For example, non‐invasive electroencephalography (EEG) signals can be used to reconstruct brain consciousness and detect eye movements for identity verification. However, physiological signal processing requires high resolution, high sensitivity, fast responses, and low power consumption, hindering practical hardware design for physiological signal processing. The bionic capability of memristor devices is very promising in the context of building physiological signal processing hardware and they have demonstrated a handful of advantages over the traditional Von Neumann architecture system in accelerating neural networks. Memristor networks can be integrated as a hardware system for physiological signal processing that can deliver higher energy efficiency and lower latency compared to traditional implementations. This review paper first introduces memristor characteristics, followed by a comprehensive literature study of memristor‐based networks. Physiology signal processing applications enabled by these integrated memristor networks are also presented in this review. In summary, this paper aims to provide a new perspective on physiological signal processing using integrated memristor networks