1 citations · 3 across the 8 of their papers we have counts for
8 papers
QoS-Nets: Adaptive Approximate Neural Network Inference
Elias Trommer, Bernd Waschneck, Akash Kumar
In order to vary the arithmetic resource consumption of neural network applications at runtime, this work proposes the flexible reuse of approximate multipliers for neural network…
Temporal Decisions: Leveraging Temporal Correlation for Efficient Decisions in Early Exit Neural Networks
Max Sponner, Lorenzo Servadei, Bernd Waschneck +2
Deep Learning is becoming increasingly relevant in Embedded and Internet-of-things applications. However, deploying models on embedded devices poses a challenge due to their resour…
Efficient Post-Training Augmentation for Adaptive Inference in Heterogeneous and Distributed IoT Environments
Max Sponner, Lorenzo Servadei, Bernd Waschneck +2
Early Exit Neural Networks (EENNs) present a solution to enhance the efficiency of neural network deployments. However, creating EENNs is challenging and requires specialized domai…
Temporal Patience: Efficient Adaptive Deep Learning for Embedded Radar Data Processing
Max Sponner, Julius Ott, Lorenzo Servadei +3
Radar sensors offer power-efficient solutions for always-on smart devices, but processing the data streams on resource-constrained embedded platforms remains challenging. This pape…
Combining Gradients and Probabilities for Heterogeneous Approximation of Neural Networks
Elias Trommer, Bernd Waschneck, Akash Kumar
This work explores the search for heterogeneous approximate multiplier configurations for neural networks that produce high accuracy and low energy consumption. We discuss the vali…
dCSR: A Memory-Efficient Sparse Matrix Representation for Parallel Neural Network Inference
Elias Trommer, Bernd Waschneck, Akash Kumar
Reducing the memory footprint of neural networks is a crucial prerequisite for deploying them in small and low-cost embedded devices. Network parameters can often be reduced signif…
Compiler Toolchains for Deep Learning Workloads on Embedded Platforms
Max Sponner, Bernd Waschneck, Akash Kumar
As the usage of deep learning becomes increasingly popular in mobile and embedded solutions, it is necessary to convert the framework-specific network representations into executab…
Small-Footprint Keyword Spotting on Raw Audio Data with Sinc-Convolutions
Simon Mittermaier, Ludwig Kürzinger, Bernd Waschneck +1
Keyword Spotting (KWS) enables speech-based user interaction on smart devices. Always-on and battery-powered application scenarios for smart devices put constraints on hardware res…