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Found 6 papers, 1 papers with code
Improving the Energy Efficiency and Robustness of tinyML Computer Vision using Log-Gradient Input Images
This paper studies the merits of applying log-gradient input images to convolutional neural networks (CNNs) for tinyML computer vision (CV).
Low-Rank Training of Deep Neural Networks for Emerging Memory Technology
The recent success of neural networks for solving difficult decision tasks has incentivized incorporating smart decision making "at the edge."
Separating the Effects of Batch Normalization on CNN Training Speed and Stability Using Classical Adaptive Filter Theory
Batch Normalization (BatchNorm) is commonly used in Convolutional Neural Networks (CNNs) to improve training speed and stability.
Low Rank Training of Deep Neural Networks for Emerging Memory Technology
The recent success of neural networks for solving difficult decision tasks has incentivized incorporating smart decision making "at the edge."
BinarEye: An Always-On Energy-Accuracy-Scalable Binary CNN Processor With All Memory On Chip in 28nm CMOS
This paper introduces BinarEye: a digital processor for always-on Binary Convolutional Neural Networks.
Convolutional Neural Networks using Logarithmic Data Representation
In this paper we propose a new data representation that enables state-of-the-art networks to be encoded to 3 bits with negligible loss in classification performance.
