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Integrating Event-based Dynamic Vision Sensors with Sparse Hyperdimensional Computing: A Low-power Accelerator with Online Capability
(2020)ISLPED '20: Proceedings of the ACM/IEEE International Symposium on Low Power Electronics and DesignWe propose to embed features extracted from event-driven dynamic vision sensors to binary sparse representations in hyperdimensional (HD) space for regression. This embedding compresses events generated across 346x260 differential pixels to a sparse 8160-bit vector by applying random activation functions. The sparse representation not only simplifies inference, but also enables online learning with the same memory footprint. Specifically, ...Conference Paper -
An Energy-efficient Localization System for Imprecisely Positioned Sensor Nodes with Flying UAVs
(2020)2020 IEEE 18th International Conference on Industrial Informatics (INDIN)Conference Paper -
A Synergistic Approach to Predictable Compilation and Scheduling on Commodity Multi-Cores
(2020)LCTES '20: The 21st ACM SIGPLAN/SIGBED Conference on Languages, Compilers, and Tools for Embedded SystemsConference Paper -
Prevention of Microarchitectural Covert Channels on an Open-Source 64-bit RISC-V Core
(2020)Conference Paper -
An Accurate EEGNet-based Motor-Imagery Brain–Computer Interface for Low-Power Edge Computing
(2020)2020 IEEE International Symposium on Medical Measurements and Applications (MeMeA)This paper presents an accurate and robust embedded motor-imagery brain–computer interface (MI-BCI). The proposed novel model, based on EEGNet [1], matches the requirements of memory footprint and computational resources of low-power microcontroller units (MCUs), such as the ARM Cortex-M family. Furthermore, the paper presents a set of methods, including temporal downsampling, channel selection, and narrowing of the classification window, ...Conference Paper -
Binary Models for Motor-Imagery Brain–Computer Interfaces: Sparse Random Projection and Binarized SVM
(2020)2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)Successful motor imagery brain–computer (MI-BCI) algorithms typically rely on a large number of features used in a classifier with real-valued weights that render them unsuitable for real-time execution on a resource-limited device. We propose a new method that randomly projects a large number of real-valued Riemannian covariance features to a binary space, where a linear SVM classifier can be learned with binary weights too. Flexibly ...Conference Paper -
Evolvable Hyperdimensional Computing: Unsupervised Regeneration of Associative Memory to Recover Faulty Components
(2020)2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)This paper proposes evolvable hyperdimensional (HD) computing to maintain high classification accuracy as permanent faults occur in emerging non-volatile memory fabrics. Our proposed HD architecture can detect, localize, and isolate faulty PCM blocks in discriminative classifiers, followed by unsupervised regeneration of new blocks to compensate accuracy loss. We demonstrate its application on a language recognition task: it is able to ...Conference Paper -
Compressing Subject-specific Brain-Computer Interface Models into One Model by Superposition in Hyperdimensional Space
(2020)2020 Design, Automation and Test in Europe Conference and Exhibition (DATE)Accurate multiclass classification of electroencephalography (EEG) signals is still a challenging task towards the development of reliable motor imagery brain-computer interfaces (MI-BCIs). Deep learning algorithms have been recently used in this area to deliver a compact and accurate model. Reaching high-level of accuracy requires to store subjects-specific trained models that cannot be achieved with an otherwise compact model trained ...Conference Paper -
Q-EEGNet: an Energy-Efficient 8-bit Quantized Parallel EEGNet Implementation for Edge Motor-Imagery Brain–Machine Interfaces
(2020)2020 IEEE International Conference on Smart Computing (SMARTCOMP)Conference Paper -
EEG-TCNet: An Accurate Temporal Convolutional Network for Embedded Motor-Imagery Brain-Machine Interfaces
(2020)2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)In recent years, deep learning (DL) has contributed significantly to the improvement of motor-imagery brain–machine interfaces (MI-BMIs) based on electroencephalography (EEG). While achieving high classification accuracy, DL models have also grown in size, requiring a vast amount of memory and computational resources. This poses a major challenge to an embedded BMI solution that guarantees user privacy, reduced latency, and low power ...Conference Paper