A survey on hyperdimensional computing aka vector symbolic architectures, part ii: Applications, cognitive models, and challenges
This is Part II of the two-part comprehensive survey devoted to a computing framework most
commonly known under the names Hyperdimensional Computing and Vector Symbolic …
commonly known under the names Hyperdimensional Computing and Vector Symbolic …
Vector symbolic architectures as a computing framework for emerging hardware
This article reviews recent progress in the development of the computing framework vector
symbolic architectures (VSA)(also known as hyperdimensional computing). This framework …
symbolic architectures (VSA)(also known as hyperdimensional computing). This framework …
Learning from hypervectors: A survey on hypervector encoding
Hyperdimensional computing (HDC) is an emerging computing paradigm that imitates the
brain's structure to offer a powerful and efficient processing and learning model. In HDC, the …
brain's structure to offer a powerful and efficient processing and learning model. In HDC, the …
Dual attention relation network with fine-tuning for few-shot EEG motor imagery classification
Recently, motor imagery (MI) electroencephalography (EEG) classification techniques using
deep learning have shown improved performance over conventional techniques. However …
deep learning have shown improved performance over conventional techniques. However …
Q-ppg: Energy-efficient ppg-based heart rate monitoring on wearable devices
Hearth Rate (HR) monitoring is increasingly performed in wrist-worn devices using low-cost
photoplethysmography (PPG) sensors. However, Motion Artifacts (MAs) caused by …
photoplethysmography (PPG) sensors. However, Motion Artifacts (MAs) caused by …
An ensemble of hyperdimensional classifiers: Hardware-friendly short-latency seizure detection with automatic iEEG electrode selection
We propose a new algorithm for detecting epileptic seizures. Our algorithm first extracts
three features, namely mean amplitude, line length, and local binary patterns that are fed to …
three features, namely mean amplitude, line length, and local binary patterns that are fed to …
Meta-health: learning-to-learn (Meta-learning) as a next generation of deep learning exploring healthcare challenges and solutions for rare disorders: a systematic …
In clinical scenarios, the two subfields of Artificial Intelligence (AI), ie, Machine Learning (ML)
and Deep Learning (DL) methods have become the de facto standard in several domains of …
and Deep Learning (DL) methods have become the de facto standard in several domains of …
Embedding temporal convolutional networks for energy-efficient ppg-based heart rate monitoring
Photoplethysmography (PPG) sensors allow for non-invasive and comfortable heart rate
(HR) monitoring, suitable for compact wrist-worn devices. Unfortunately, motion artifacts …
(HR) monitoring, suitable for compact wrist-worn devices. Unfortunately, motion artifacts …
Hyper-dimensional computing challenges and opportunities for AI applications
Brain-inspired architectures are gaining increased attention, especially for edge devices to
perform cognitive tasks utilizing its limited energy budget and computing resources …
perform cognitive tasks utilizing its limited energy budget and computing resources …
Demeter: A fast and energy-efficient food profiler using hyperdimensional computing in memory
Food profiling is an essential step in any food monitoring system needed to prevent health
risks and potential frauds in the food industry. Significant improvements in sequencing …
risks and potential frauds in the food industry. Significant improvements in sequencing …