213 citations · 934 across the 47 of their papers we have counts for
8 papers · 1 filter
Batch Normalization Sampling
Zhaodong Chen, Lei Deng, Guoqi Li +4
Deep Neural Networks (DNNs) thrive in recent years in which Batch Normalization (BN) plays an indispensable role. However, it has been observed that BN is costly due to the reducti…
Dynamic Sparse Graph for Efficient Deep Learning
Liu Liu, Lei Deng, Xing Hu +4
We propose to execute deep neural networks (DNNs) with dynamic and sparse graph (DSG) structure for compressive memory and accelerative execution during both training and inference…
In-memory multiplication engine with SOT-MRAM based stochastic computing
Xin Ma, Liang Chang, Shuangchen Li +3
Processing-in-memory (PIM) turns out to be a promising solution to breakthrough the memory wall and the power wall. While prior PIM designs yield successful implementation of bitwi…
Tackling the Qubit Mapping Problem for NISQ-Era Quantum Devices
Gushu Li, Yufei Ding, Yuan Xie
Due to little consideration in the hardware constraints, e.g., limited connections between physical qubits to enable two-qubit gates, most quantum algorithms cannot be directly exe…
Crossbar-aware neural network pruning
Ling Liang, Lei Deng, Yueling Zeng +5
Crossbar architecture based devices have been widely adopted in neural network accelerators by taking advantage of the high efficiency on vector-matrix multiplication (VMM) operati…
Structurally Sparsified Backward Propagation for Faster Long Short-Term Memory Training
Maohua Zhu, Jason Clemons, Jeff Pool +3
Exploiting sparsity enables hardware systems to run neural networks faster and more energy-efficiently. However, most prior sparsity-centric optimization techniques only accelerate…
L1-Norm Batch Normalization for Efficient Training of Deep Neural Networks
Shuang Wu, Guoqi Li, Lei Deng +3
Batch Normalization (BN) has been proven to be quite effective at accelerating and improving the training of deep neural networks (DNNs). However, BN brings additional computation,…
Bridging the Gap Between Neural Networks and Neuromorphic Hardware with A Neural Network Compiler
Yu Ji, YouHui Zhang, WenGuang Chen +1
Different from developing neural networks (NNs) for general-purpose processors, the development for NN chips usually faces with some hardware-specific restrictions, such as limited…