Emergent ReRAM-based accelerators support in-memory computation to accelerate deep
neural network (DNN) inference. Weight matrix pruning of DNNs is a widely used technique …

Sparsity is an intrinsic property of convolutional neural networks (CNNs), worth exploiting for
CNN accelerators. However, the extra processing involved comes with hardware overhead …

Emerging ReRAM-based accelerators support in-memory computation to accelerate deep
neural network (DNN) inference. Weight matrix pruning is a widely used technique to reduce …

Processing-in-memory (PIM) architectures have been highlighted as one of the viable
solutions for faster and more power-efficient convolutional neural networks (CNNs) …

Recent years have seen a paradigm shift towards multi-task learning. This calls for memory
and energy-efficient solutions for inference in a multi-task scenario. We propose an …

Currently, the sparsity of weights and activations are mainly utilized to improve the energy
efficiency and computational performance of CNN accelerators. However, the irregular …

Processing-in-memory (PIM) architectures have been highlighted as one of the most viable
options for faster and more power-efficient computation. Paired with a convolutional weight …

Since the introduction of distantly supervised relation extraction methods, numerous
approaches have been developed, the most representative of which is multi-instance …

Resistive random access memory (RRAM) is an emerging device for processing-in-memory
(PIM) architecture to accelerate convolutional neural network (CNN). However, due to the …

FPGA-based accelerators have shown great potential in improving the performance of CNN
inference. However, the existing FPGA-based approaches suffer from a low compute unit …