An energy-efficient spiking neural network for finger velocity decoding for implantable brain-machine interface
Brain-machine interfaces (BMIs) are promising for motor rehabilitation and mobility
augmentation. High-accuracy and low-power algorithms are required to achieve implantable …
augmentation. High-accuracy and low-power algorithms are required to achieve implantable …
A 40-nm 1.89-pJ/SOP Scalable Convolutional Spiking Neural Network Learning Core With On-Chip Spatiotemporal Back-Propagation
In recent years, progress in spiking neural network (SNN) research has generated growing
interest in specialized SNN hardware. However, most of the hardware studies are about …
interest in specialized SNN hardware. However, most of the hardware studies are about …
Colibriuav: An ultra-fast, energy-efficient neuromorphic edge processing uav-platform with event-based and frame-based cameras
The interest in dynamic vision sensor (DVS)-powered unmanned aerial vehicles (UAV) is
raising, especially due to the microsecond-level reaction time of the bio-inspired event …
raising, especially due to the microsecond-level reaction time of the bio-inspired event …
A low-bitwidth integer-stbp algorithm for efficient training and inference of spiking neural networks
Spiking neural networks (SNNs) that enable energy-efficient neuromorphic hardware are
receiving growing attention. Training SNNs directly with back-propagation has demonstrated …
receiving growing attention. Training SNNs directly with back-propagation has demonstrated …
A Spiking Neural Network Decoder for Implantable Brain Machine Interfaces and its Sparsity-aware Deployment on RISC-V Microcontrollers
J Liao, O Toomey, X Wang, L Widmer… - arXiv preprint arXiv …, 2024 - arxiv.org
Implantable Brain-machine interfaces (BMIs) are promising for motor rehabilitation and
mobility augmentation, and they demand accurate and energy-efficient algorithms. In this …
mobility augmentation, and they demand accurate and energy-efficient algorithms. In this …
BPLC+ NOSO: backpropagation of errors based on latency code with neurons that only spike once at most
SM Jin, D Kim, DH Yoo, J Eshraghian… - Complex & Intelligent …, 2023 - Springer
For mathematical completeness, we propose an error-backpropagation algorithm based on
latency code (BPLC) with spiking neurons conforming to the spike–response model but …
latency code (BPLC) with spiking neurons conforming to the spike–response model but …
Spike-Weighted Spiking Neural Network with Spiking Long Short-Term Memory: A Biomimetic Approach to Decoding Brain Signals
Background. Brain–machine interfaces (BMIs) offer users the ability to directly communicate
with digital devices through neural signals decoded with machine learning (ML)-based …
with digital devices through neural signals decoded with machine learning (ML)-based …
Spiking-HDC: A Spiking Neural Network Processor with HDC Classifier Enabling Transfer Learning
A Xiao, X Zhang, J Yang, L Zheng… - 2024 IEEE International …, 2024 - ieeexplore.ieee.org
This work proposes Spiking-HDC, a spiking neural network (SNN) processing system with
hyperdimensional computing (HDC) and its hardware design for domain transfer scenarios …
hyperdimensional computing (HDC) and its hardware design for domain transfer scenarios …
A Lightweight Integer-STBP On-Chip Learning Method of Spiking Neural Networks For Edge Processors
Spiking Neural Networks (SNNs) with energy-efficient on neuromorphic hardware are
suitable for edge processors with limited resources. The software-hardware co-design plays …
suitable for edge processors with limited resources. The software-hardware co-design plays …
[PDF][PDF] ColibriUAV: Towards an Ultra-Fast, Energy-Efficient Neuromorphic edge-processing platform UAV with Event-Based and Frame-Based Cameras
The interest in dynamic vision sensor (DVS)-powered unmanned aerial vehicles (UAV) is
raising, especially due to the microsecond-level reaction time of the bio-inspired event …
raising, especially due to the microsecond-level reaction time of the bio-inspired event …