<i>Retracted on January 26, 2021</i>
            : 3D-based video recognition acceleration by leveraging temporal locality

CNN-based stereo matching methods achieve great performance but come with high computational requirements. Pruning a CNN can reduce the complexity but may in turn lead to memory conflicts, lowering throughput. Our proposed architecture and memory mapping technique aim at reducing conflicts to exploit extremely sparse stereo matching networks. To maintain a high utilization of processing elements, we decompose the de-convolution operation into several convolution operations. The proposed architecture provides a 2.1× speed up over SCNN. Compared to the software implementation, only 0.01% performance drop is observed, so that the proposed architecture obtains state-of-the-art accuracy compared to existing sparsity aware hardware implementations.

show abstract

VR-DANN: Real-Time Video Recognition via Decoder-Assisted Neural Network Acceleration

Song

Liu

et al. 2020

2020 53rd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO)

View full text Add to dashboard Cite

Retracted on January 26, 2021 : 3D-based video recognition acceleration by leveraging temporal locality

Cited by 4 publications

References 22 publications

An Energy-Efficient Low-Latency 3D-CNN Accelerator Leveraging Temporal Locality, Full Zero-Skipping, and Hierarchical Load Balance

An Energy-Efficient Low-Latency 3D-CNN Accelerator Leveraging Temporal Locality, Full Zero-Skipping, and Hierarchical Load Balance

A Computational Efficient Architecture for Extremely Sparse Stereo Network

VR-DANN: Real-Time Video Recognition via Decoder-Assisted Neural Network Acceleration

Contact Info

Product

Resources

About