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EIE: Efficient Inference Engine on Compressed Deep Neural Network
An energy efficient inference engine (EIE) that performs inference on this compressed network model and accelerates the resulting sparse matrix-vector multiplication with weight sharing and is 189x and 13x faster when compared to CPU and GPU implementations of the same DNN without compression. Expand
Exploring the Regularity of Sparse Structure in Convolutional Neural Networks
This paper quantitatively measure the trade-off between sparsity regularity and prediction accuracy, providing insights in how to maintain accuracy while having more a more structured sparsity pattern. Expand
Efficient Sparse-Winograd Convolutional Neural Networks
Two modifications to Winograd-based CNNs are proposed to enable these methods to exploit sparsity, including moving the ReLU operation into the Winog Rad domain to increase the sparsity of the transformed activations and prune the weights in the Winogsrad domain to exploit static weight sparsity. Expand
Exploring the Granularity of Sparsity in Convolutional Neural Networks
This analysis, based on the framework of a recent sparse convolutional neural network (SCNN) accelerator, demonstrates that coarse-grained sparsity saves 30% – 35% of memory references compared with fine-graining sparsity. Expand
A Statistical Test of Phase Closure to Detect Influences on DInSAR Deformation Estimates Besides Displacements and Decorrelation Noise: Two Case Studies in High-Latitude Regions
An asymptotic Wald significance test is proposed, which detects situations when the observed closure error cannot solely be explained by noise, and indicates the presence of processes that can have systematic and deleterious impacts on the estimation of surface movements. Expand
Pruning of Winograd and FFT Based Convolution Algorithm
Winogradand FFT-based convolution are two efficient convolution algorithms targeting high-performance inference. Their efficiency comes from the reduction of the number of multiplication operationsExpand
Deep compression and EIE: Efficient inference engine on compressed deep neural network
This article consists only of a collection of slides from the author's conference presentation.
FastTree: A hardware KD-tree construction acceleration engine for real-time ray tracing
Experimental result shows FastTree outperforms existing hardware construction engines by a factor of nearly 4X at a similar area and power budget. Expand
Head pose Estimation Using Convolutional Neural Networks
Head pose estimation is a fundamental problem in computer vision. Several methods has been proposed to solve this problem. Most existing methods use traditional computer vision methods and existingExpand
Video salient object detection via cross-frame cellular automata
This paper proposes a novel salient object detection method for videos based on cross-frame cellular automata that outperforms the state-of-the-art methods on different types of videos. Expand