ArticleNature communications2024
Spike-based dynamic computing with asynchronous sensing-computing neuromorphic chip.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 2 of them syntheses that pooled it.
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Who cites it
27 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Advancements in Machine Learning-Assisted Flexible Electronics: Technologies, Applications, and Future Prospects.Biosensors · 2026Pooled it
- Evolution of brain-computer interface technologies for stroke rehabilitation: a bibliometric integration of neural decoding and functional recovery (2016-2025).Frontiers in neuroscience · 2026Pooled it
- Solution-Processed Multi-Junction Optoelectronic Synapses for Mode-Switchable SWIR Neuromorphic Vision Sensing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Analysis and Applications of Neuromorphic Memristors in Artificial Intelligence Computing.Nano-micro letters · 2026Review
- Voltage-Programmable Switching in Halide Perovskite Memristors by Asymmetric Contact Engineering.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- Amphibian-inspired neuromorphic dynamic vision systems based on ferroelectric field-effect transistor.Nature communications · 2026Article
- In-Sensor-Memory Computing for Post-Von Neumann Intelligence: A Perspective.Nano-micro letters · 2026Article
- Transistor-Level Activation Functions via Two-Gate Designs: From Analog Sigmoid and Gaussian Control to Real-Time Hardware Demonstrations.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- NEOSTI - a neuromorphic electronic-opto spatial-temporal hybrid image sensor.Nature communications · 2026Article
- A highly energy-efficient multi-core neuromorphic architecture for training deep spiking neural networks.Nature communications · 2026Article
- Owl-vision-inspired near sensor computing.Nature communications · 2026Article
- Bioinspired spiking architecture enables energy constrained touch encoding.Nature communications · 2026Article
- Bidirectional cross-day alignment of neural spikes and behavior using a hybrid SNN-ANN algorithm.Frontiers in neuroscience · 2026Article
- From deep brain stimulation to brain-computer interfaces: current progress in implantable neurotechnology.Frontiers in neuroscience · 2026Review
- Neuromorphic computing paradigms enhance robustness through spiking neural networks.Nature communications · 2025Article
- Mapping EEG Metrics to Human Affective and Cognitive Models: An Interdisciplinary Scoping Review from a Cognitive Neuroscience Perspective.Biomimetics (Basel, Switzerland) · 2025Review
- Large-scale crossbar arrays based on three-terminal MoSNature communications · 2025Article
- Hardware, Algorithms, and Applications of the Neuromorphic Vision Sensor: A Review.Sensors (Basel, Switzerland) · 2025Review
- A multisynaptic spiking neuron for simultaneously encoding spatiotemporal dynamics.Nature communications · 2025Article
- Event driven neural network on a mixed signal neuromorphic processor for EEG based epileptic seizure detection.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
18 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
By mimicking the neurons and synapses of the human brain and employing spiking neural networks on neuromorphic chips, neuromorphic computing offers a promising energy-efficient machine intelligence. How to borrow high-level brain dynamic mechanisms to help neuromorphic computing achieve energy advantages is a fundamental issue. This work presents an application-oriented algorithm-software-hardware co-designed neuromorphic system for this issue. First, we design and fabricate an asynchronous chip called "Speck", a sensing-computing neuromorphic system on chip. With the low processor resting power of 0.42mW, Speck can satisfy the hardware requirements of dynamic computing: no-input consumes no energy. Second, we uncover the "dynamic imbalance" in spiking neural networks and develop an attention-based framework for achieving the algorithmic requirements of dynamic computing: varied inputs consume energy with large variance. Together, we demonstrate a neuromorphic system with real-time power as low as 0.70mW. This work exhibits the promising potentials of neuromorphic computing with its asynchronous event-driven, sparse, and dynamic nature.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.