Domain-specific hardware is becoming a promising topic in the backdrop of improvement
slow down for general-purpose processors due to the foreseeable end of Moore's Law …

We introduce channel permutations as a method to maximize the accuracy of N: M sparse
networks. N: M sparsity requires N out of M consecutive elements to be zero and has been …

Though network pruning receives popularity in reducing the complexity of convolutional
neural networks (CNNs), it remains an open issue to concurrently maintain model accuracy …

We focus on addressing the dense backward propagation issue for training efficiency of N:
M fine-grained sparsity that preserves at most N out of M consecutive weights and achieves …

State-of-the-art convolutional neural networks (CNNs) yield record-breaking predictive
performance, yet at the cost of high-energy-consumption inference, that prohibits their widely …

Neurologically, filter pruning is a procedure of forgetting and remembering recovering.
Prevailing methods directly forget less important information from an unrobust baseline at …

The success of CNNs for various vision tasks has been accompanied by a significant
increase in required FLOPs and parameter quantities, which has impeded the deployment of …

Sparsification and low-rank decomposition are two important techniques to compress deep
neural network (DNN) models. To date, these two popular yet distinct approaches are …

Processing-in-memory (PIM) architecture based on resistive random access memory
(ReRAM) crossbars is a promising solution to the memory bottleneck that long short-term …

Neural network pruning, which can be divided into unstructured pruning and structured
pruning strategies, has been proven to be an efficient method to substantially reduce the …