213 citations · 934 across the 47 of their papers we have counts for
16 papers · 1 filter
LOSTIN: Logic Optimization via Spatio-Temporal Information with Hybrid Graph Models
Nan Wu, Jiwon Lee, Yuan Xie +1
Despite the stride made by machine learning (ML) based performance modeling, two major concerns that may impede production-ready ML applications in EDA are stringent accuracy requi…
Load-balanced Gather-scatter Patterns for Sparse Deep Neural Networks
Fei Sun, Minghai Qin, Tianyun Zhang +6
Deep neural networks (DNNs) have been proven to be effective in solving many real-life problems, but its high computation cost prohibits those models from being deployed to edge de…
Understanding GNN Computational Graph: A Coordinated Computation, IO, and Memory Perspective
Hengrui Zhang, Zhongming Yu, Guohao Dai +4
Graph Neural Networks (GNNs) have been widely used in various domains, and GNNs with sophisticated computational graph lead to higher latency and larger memory consumption. Optimiz…
A Survey of Machine Learning for Computer Architecture and Systems
Nan Wu, Yuan Xie
It has been a long time that computer architecture and systems are optimized for efficient execution of machine learning (ML) models. Now, it is time to reconsider the relationship…
SmartExchange: Trading Higher-cost Memory Storage/Access for Lower-cost Computation
Yang Zhao, Xiaohan Chen, Yue Wang +6
We present SmartExchange, an algorithm-hardware co-design framework to trade higher-cost memory storage/access for lower-cost computation, for energy-efficient inference of deep ne…
TIMELY: Pushing Data Movements and Interfaces in PIM Accelerators Towards Local and in Time Domain
Weitao Li, Pengfei Xu, Yang Zhao +3
Resistive-random-access-memory (ReRAM) based processing-in-memory (RPIM) accelerators show promise in bridging the gap between Internet of Thing devices' constrained resources…
Computation on Sparse Neural Networks: an Inspiration for Future Hardware
Fei Sun, Minghai Qin, Tianyun Zhang +3
Neural network models are widely used in solving many challenging problems, such as computer vision, personalized recommendation, and natural language processing. Those models are…
A Comprehensive and Modularized Statistical Framework for Gradient Norm Equality in Deep Neural Networks
Zhaodong Chen, Lei Deng, Bangyan Wang +2
In recent years, plenty of metrics have been proposed to identify networks that are free of gradient explosion and vanishing. However, due to the diversity of network components an…
DARB: A Density-Aware Regular-Block Pruning for Deep Neural Networks
Ao Ren, Tao Zhang, Yuhao Wang +5
The rapidly growing parameter volume of deep neural networks (DNNs) hinders the artificial intelligence applications on resource constrained devices, such as mobile and wearable de…
Training High-Performance and Large-Scale Deep Neural Networks with Full 8-bit Integers
Yukuan Yang, Shuang Wu, Lei Deng +3
Deep neural network (DNN) quantization converting floating-point (FP) data in the network to integers (INT) is an effective way to shrink the model size for memory saving and simpl…
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…
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,…
CNNLab: a Novel Parallel Framework for Neural Networks using GPU and FPGA-a Practical Study with Trade-off Analysis
Maohua Zhu, Liu Liu, Chao Wang +1
Designing and implementing efficient, provably correct parallel neural network processing is challenging. Existing high-level parallel abstractions like MapReduce are insufficientl…
DLAU: A Scalable Deep Learning Accelerator Unit on FPGA
Chao Wang, Qi Yu, Lei Gong +3
As the emerging field of machine learning, deep learning shows excellent ability in solving complex learning problems. However, the size of the networks becomes increasingly large…