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Found 7 papers, 0 papers with code
DBQ: A Differentiable Branch Quantizer for Lightweight Deep Neural Networks
While various complexity reduction techniques, such as lightweight network architecture design and parameter quantization, have been successful in reducing the cost of implementing these networks, these methods have often been considered orthogonal.
HarDNN: Feature Map Vulnerability Evaluation in CNNs
As Convolutional Neural Networks (CNNs) are increasingly being employed in safety-critical applications, it is important that they behave reliably in the face of hardware errors.
Accumulation Bit-Width Scaling For Ultra-Low Precision Training Of Deep Networks
Observing that a bad choice for accumulation precision results in loss of information that manifests itself as a reduction in variance in an ensemble of partial sums, we derive a set of equations that relate this variance to the length of accumulation and the minimum number of bits needed for accumulation.
Per-Tensor Fixed-Point Quantization of the Back-Propagation Algorithm
The high computational and parameter complexity of neural networks makes their training very slow and difficult to deploy on energy and storage-constrained computing systems.
Analytical Guarantees on Numerical Precision of Deep Neural Networks
We focus on numerical precision – a key parameter defining the complexity of neural networks.
Understanding the Energy and Precision Requirements for Online Learning
Lower bounds on the data precision are derived in terms of the the desired classification accuracy and precision of the hyperparameters used in the classifier.
Error-Resilient Machine Learning in Near Threshold Voltage via Classifier Ensemble
In this paper, we present the design of error-resilient machine learning architectures by employing a distributed machine learning framework referred to as classifier ensemble (CE).
