Search Results for author:
Found 6 papers, 2 papers with code
EEGsig: an open-source machine learning-based toolbox for EEG signal processing
In the quest to realize a comprehensive EEG signal processing framework, in this paper, we demonstrate a toolbox and graphic user interface, EEGsig, for the full process of EEG signals.
DRiLLS: Deep Reinforcement Learning for Logic Synthesis
Logic synthesis requires extensive tuning of the synthesis optimization flow where the quality of results (QoR) depends on the sequence of optimizations used.
Flexible Deep Neural Network Processing
In addition, DNNs are typically deployed in ensemble to boost accuracy performance, which further exacerbates the system requirements.
Hardware-Software Codesign of Accurate, Multiplier-free Deep Neural Networks
In addition, we propose a hardware accelerator design to achieve low-power, low-latency inference with insignificant degradation in accuracy.
Understanding the Impact of Precision Quantization on the Accuracy and Energy of Neural Networks
While a large number of dedicated hardware using different precisions has recently been proposed, there exists no comprehensive study of different bit precisions and arithmetic in both inputs and network parameters.
Runtime Configurable Deep Neural Networks for Energy-Accuracy Trade-off
We present a novel dynamic configuration technique for deep neural networks that permits step-wise energy-accuracy trade-offs during runtime.
