Search Results for author:
Found 5 papers, 1 papers with code
Efficient and Low-Footprint Object Classification using Spatial Contrast
Binarized MicronNet achieves an F1-score of 94. 4% using spatial contrast, compared to only 56. 3% when using RGB input images.
Custom DNN using Reward Modulated Inverted STDP Learning for Temporal Pattern Recognition
Temporal spike recognition plays a crucial role in various domains, including anomaly detection, keyword spotting and neuroscience.
Corticomorphic Hybrid CNN-SNN Architecture for EEG-based Low-footprint Low-latency Auditory Attention Detection
In this paper, we propose a hybrid convolutional neural network-spiking neural network (CNN-SNN) corticomorphic architecture, inspired by the auditory cortex, which uses EEG data along with multi-speaker speech envelopes to successfully decode auditory attention with low latency down to 1 second, using only 8 EEG electrodes strategically placed close to the auditory cortex, at a significantly higher accuracy of 91. 03%, compared to the state-of-the-art.
NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems
The NeuroBench framework introduces a common set of tools and systematic methodology for inclusive benchmark measurement, delivering an objective reference framework for quantifying neuromorphic approaches in both hardware-independent (algorithm track) and hardware-dependent (system track) settings.
Edge AI without Compromise: Efficient, Versatile and Accurate Neurocomputing in Resistive Random-Access Memory
Realizing today's cloud-level artificial intelligence functionalities directly on devices distributed at the edge of the internet calls for edge hardware capable of processing multiple modalities of sensory data (e. g. video, audio) at unprecedented energy-efficiency.
