Reservoir computing (RC) can compute temporal data with low training cost. To enhance
data processing capability, high dimensionality of reservoir is required, which poses a …

Time‐delayed reservoir computing with marked strengths of friendly hardware implementation
and low training cost is regarded as a promising solution to realize time and energy‐…

This study proposes an 2T1R Transpose RRAM (T-RRAM) macro supports highly efficient
transpose accessibility featuring (1) a 2T1R cell with low-power near-threshold-voltage (NTV) …

The Echo State Networks (ESNs) is a class of recurrent neural network (RNN), which can
significantly reduce the training complexity since the input layer and middle layer (reservoir) …

In this work, we realized a three-dimensional (3D) reservoir computing (RC) by utilizing the
IV nonlinearity and short-term memory of the dynamic memristor in 4-layer vertical array. The …

We developed a unified physical and statistical compact model of Bias Temperature Instability
(BTI) effects on scaling technology nodes towards robust VLSI design, with an excessive …

We proposed a state transition probability model based on Hidden Markov Model (HMM),
which can predict the lifetime for different endurance failure modes. The prediction span of this …

Three-dimensional reservoir computing (3D RC) system is an energy efficient recurrent neural
network for achieving high area efficiency. Considering the physical implementation of 3D …

In embedded neuromorphic Internet of Things (IoT) systems, it is critical to improve the
efficiency of neural network (NN) edge devices in inferring a pretrained NN. Meanwhile, in the …

In this work, a machine learning‐assisted prediction model is proposed to analyze the
reliability issues in the 28 nm resistive random access memory (RRAM) chip with raw data …