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Found 5 papers, 2 papers with code
On Resource-Efficient Bayesian Network Classifiers and Deep Neural Networks
We present two methods to reduce the complexity of Bayesian network (BN) classifiers.
Resource-Efficient Speech Mask Estimation for Multi-Channel Speech Enhancement
While machine learning techniques are traditionally resource intensive, we are currently witnessing an increased interest in hardware and energy efficient approaches.
On the Difficulty of Designing Processor Arrays for Deep Neural Networks
We present an analysis of popular DNN models to illustrate how it can estimate required cycles, data movement costs, as well as systolic array utilization, and show how the progress in network architecture design impacts the efficiency of inference on accelerators based on systolic arrays.
Resource-Efficient Neural Networks for Embedded Systems
While machine learning is traditionally a resource intensive task, embedded systems, autonomous navigation, and the vision of the Internet of Things fuel the interest in resource-efficient approaches.
N-Ary Quantization for CNN Model Compression and Inference Acceleration
In this work we propose a method for weight and activation quantization that is scalable in terms of quantization levels (n-ary representations) and easy to compute while maintaining the performance close to full-precision CNNs.
