9 citations · 17 across the 3 of their papers we have counts for
7 papers
Recurrent Neural Network Control of a Hybrid Dynamic Transfemoral Prosthesis with EdgeDRNN Accelerator
Chang Gao, Rachel Gehlhar, Aaron D. Ames +2
Lower leg prostheses could improve the life quality of amputees by increasing comfort and reducing energy to locomote, but currently control methods are limited in modulating behav…
EdgeDRNN: Enabling Low-latency Recurrent Neural Network Edge Inference
Chang Gao, Antonio Rios-Navarro, Xi Chen +2
This paper presents a Gated Recurrent Unit (GRU) based recurrent neural network (RNN) accelerator called EdgeDRNN designed for portable edge computing. EdgeDRNN adopts the spiking…
Closing the Accuracy Gap in an Event-Based Visual Recognition Task
Bodo Rückauer, Nicolas Känzig, Shih-Chii Liu +2
Mobile and embedded applications require neural networks-based pattern recognition systems to perform well under a tight computational budget. In contrast to commonly used synchron…
Event-based Vision: A Survey
Guillermo Gallego, Tobi Delbruck, Garrick Orchard +8
Event cameras are bio-inspired sensors that differ from conventional frame cameras: Instead of capturing images at a fixed rate, they asynchronously measure per-pixel brightness ch…
PRED18: Dataset and Further Experiments with DAVIS Event Camera in Predator-Prey Robot Chasing
Diederik Paul Moeys, Daniel Neil, Federico Corradi +7
Machine vision systems using convolutional neural networks (CNNs) for robotic applications are increasingly being developed. Conventional vision CNNs are driven by camera frames at…
ADaPTION: Toolbox and Benchmark for Training Convolutional Neural Networks with Reduced Numerical Precision Weights and Activation
Moritz B. Milde, Daniel Neil, Alessandro Aimar +2
Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs) are useful for many practical tasks in machine learning. Synaptic weights, as well as neuron activation functio…