Search
Results
-
StoneNode: A low-power sensor device for induced rockfall experiments
(2017)2017 IEEE Sensors Applications Symposium (SAS)Conference Paper -
Rat cortical layers classification extracting evoked local field potential images with implanted multi-electrode sensor
(2018)2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom)One of the most ambitious goals of neuroscience and its neuroprosthetic applications is to interface intelligent electronic devices with the biological brain to cure neurological diseases. This emerging research field builds on our growing understanding of brain circuits and on recent technological advances in miniaturization of implantable multi-electrode-arrays (MEAs) to record brain signals at high spatiotemporal resolution. Data ...Conference Paper -
Embedded Classification of Local Field Potentials Recorded from Rat Barrel Cortex with Implanted Multi-Electrode Array
(2018)2018 IEEE Biomedical Circuits and Systems Conference (BioCAS)This paper focuses on ultra-low power embedded classification of neural activities. The machine learning (ML) algorithm has been trained using evoked local field potentials (LFPs) recorded with an implanted 16x16 multi-electrode array (MEA) from the rat barrel cortex while stimulating the whisker. Experimental results demonstrate that ML can be successfully applied to noisy single-trial LFPs. We achieved up to 95.8% test accuracy in ...Conference Paper -
CAS-CNN: A deep convolutional neural network for image compression artifact suppression
(2017)2017 International Joint Conference on Neural Networks (IJCNN)Conference Paper -
Fast and Accurate Multiclass Inference for MI-BCIs Using Large Multiscale Temporal and Spectral Features
(2018)2018 26th European Signal Processing Conference (EUSIPCO)Accurate, fast, and reliable multiclass classification of electroencephalography (EEG) signals is a challenging task towards the development of motor imagery brain–computer interface (MI-BCI) systems. We propose enhancements to different feature extractors, along with a support vector machine (SVM) classifier, to simultaneously improve classification accuracy and execution time during training and testing. We focus on the well-known common ...Conference Paper -
Accelerating real-time embedded scene labeling with convolutional networks
(2015)Proceedings of the 52nd Annual Design Automation Conference (DAC '15)Conference Paper -
CBinfer: Change-Based Inference for Convolutional Neural Networks on Video Data
(2017)Proceedings of the 11th International Conference on Distributed Smart Cameras (ICDSC 2017)Conference Paper -
Impact of temporal subsampling on accuracy and performance in practical video classification
(2017)2017 25th European Signal Processing Conference (EUSIPCO)In this paper we evaluate three state-of-the-art neural-network-based approaches for large-scale video classification, where the computational efficiency of the inference step is of particular importance due to the ever increasing amount of data throughput for video streams. Our evaluation focuses on finding good efficiency vs. accuracy tradeoffs by evaluating different network configurations and parameterizations. In particular, we ...Conference Paper -
CHIPMUNK : A Systolically Scalable 0.9 mm2, 3.08 Gop/s/mW @ 1.2 mW Accelerator for Near-Sensor Recurrent Neural Network Inference
(2018)2018 IEEE Custom Integrated Circuits Conference (CICC)Recurrent neural networks (RNNs) are state-of-the-art in voice awareness/understanding and speech recognition. On-device computation of RNNs on low-power mobile and wearable devices would be key to applications such as zero-latency voice-based human-machine interfaces. Here we present CHIPMUNK, a small (<;1 mm 2 ) hardware accelerator for Long-Short Term Memory RNNs in UMC 65 nm technology capable to operate at a measured peak efficiency ...Conference Paper