Artificial intelligence (AI) edge devices prefer employing high-capacity nonvolatile compute-
in-memory (CIM) to achieve high energy efficiency and rapid wakeup-to-response with …

Analog in-memory computing (AIMC) leverages the inherent physical characteristics of
resistive memory devices to execute computational operations, notably matrix-vector …

Artificial intelligence (AI) edge devices increasingly require the enhanced accuracy of
floating-point (FP) multiply-and-accumulate (MAC) operations as well as nonvolatile on-chip …

The integration of serverless edge computing and the Industrial Internet of Things (IIoT) has
the potential to optimize industrial production. However, cold start latency is one of the main …

Artificial neural networks have led to a higher computational burden, complicating inference
tasks on low-power edge devices. Spiking neural network (SNN), which leverages sparse …

While memory-augmented neural networks (MANNs) offer an effective solution for few-shot
learning (FSL) by integrating deep neural networks with external memory, the capacity …

ReRAM-based accelerators have become prevalent in accelerating deep neural network
inference owing to their in-situ computing capability of ReRAM crossbars. However, most …

Emerging resistive technologies include several nonlinear devices with the capability of
changing their resistive state based on the voltage (/current) across (/flowing through) the …

Computing-in-memory (CIM) can suppress the energy consumed in transferring data from
the memory to the processor. However, implementation of CIMs is often impractical due to …