Machine learning (ML) models are widely used in many important domains. For efficiently
processing these computational-and memory-intensive applications, tensors of these …

Recent developments in deep neural network (DNN) pruning introduces data sparsity to
enable deep learning applications to run more efficiently on resourceand energy …

Sparse matrix-matrix multiplication (SpGEMM) is widely used in many scientific and deep
learning applications. The highly irregular structures of SpGEMM limit its performance and …

Deep neural networks (DNNs) are widely used in many fields, such as artificial intelligence
generated content (AIGC) and robotics. To efficiently support these tasks, the model pruning …

We present an energy-efficient processing-in-memory (PIM) architecture named Z-PIM that
supports both sparsity handling and fully variable bit-precision in weight data for energy …

This article proposes boosting the multiplication performance for convolutional neural
network (CNN) inference using a precision prediction preprocessor which controls various …

Existing deep convolutional neural networks (CNNs) generate massive interlayer feature
data during network inference. To maintain real-time processing in embedded systems …

Convolution neural network (CNN) with pruning techniques has shown remarkable
prospects in electrocardiogram (ECG) classification. However, efficiently deploying the …

We present a 3.1 POp/s/W fully digital hardware accelerator for ternary neural networks
(TNNs). CUTIE, the completely unrolled ternary inference engine, focuses on minimizing …

Transfer learning, which transfers knowledge from source datasets to target datasets, is
practical for adaptive deep neural network (DNN) applications. When considering user …