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Found 6 papers, 2 papers with code
GhostShiftAddNet: More Features from Energy-Efficient Operations
When compared to GhostNet, inference latency on the Jetson Nano is improved by 1. 3x and 2x on the GPU and CPU respectively.
Dynamic Transformer for Efficient Machine Translation on Embedded Devices
The new reduced design space results in a BLEU score increase of approximately 1% for sub-optimal models from the original design space, with a wide range for performance scaling between 0. 356s - 1. 526s for the GPU and 2. 9s - 7. 31s for the CPU.
Dynamic-OFA: Runtime DNN Architecture Switching for Performance Scaling on Heterogeneous Embedded Platforms
However, the training process of such dynamic DNNs can be costly, since platform-aware models of different deployment scenarios must be retrained to become dynamic.
A Variance Controlled Stochastic Method with Biased Estimation for Faster Non-convex Optimization
In this paper, we proposed a new technique, {\em variance controlled stochastic gradient} (VCSG), to improve the performance of the stochastic variance reduced gradient (SVRG) algorithm.
A Stochastic Gradient Method with Biased Estimation for Faster Nonconvex Optimization
This paper proposes an integrated approach which can control the nature of the stochastic element in the optimizer and can balance the trade-off of estimator between the biased and unbiased by using a hyper-parameter.
Sparse Regularized Deep Neural Networks For Efficient Embedded Learning
Deep learning is becoming more widespread in its application due to its power in solving complex classification problems.
