ArticleProceedings of the National Academy of Sciences of the United States of America2016
Convolutional networks for fast, energy-efficient neuromorphic computing.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 108 papers.
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Who cites it
108 citing papers in PubMed, 782 citations in OpenAlex.
- Higher-order neuromorphic Ising machines-autoencoders and Fowler-Nordheim annealers are all you need for scalability.Nature communications · 2026Article
- PS-SNN: pattern separation learning for expandable spiking neural networks in class-incremental learning.Scientific reports · 2026Article
- Organic Phototransistor Photonic Synapses for Artificial Vision.Nano-micro letters · 2026Review
- Learned adaptive properties for mitigation of weight perturbations in embedded spiking networks.Frontiers in neuroscience · 2026Article
- The cost of unmodeled biological complexity in artificial neural networks.Patterns (New York, N.Y.) · 2025Review
- Ternary Logic Design Based on Novel Tunneling-Drift-Diffusion Field-Effect Transistors.Nanomaterials (Basel, Switzerland) · 2025Article
- Quantum-limited stochastic optical neural networks operating at a few quanta per activation.Nature communications · 2025Article
- Fine spatial-temporal density mapping with optimized approaches for many-core system.Frontiers in neuroscience · 2025Article
- The backpropagation algorithm implemented on spiking neuromorphic hardware.Nature communications · 2024Article
- Neuromorphic neuromodulation: Towards the next generation of closed-loop neurostimulation.PNAS nexus · 2024Review
- Optical neural networks: progress and challenges.Light, science & applications · 2024Review
- Spiking neural networks for physiological and speech signals: a review.Biomedical engineering letters · 2024Review
- Synergistically Modulating Conductive Filaments in Ion-Based Memristors for Enhanced Analog In-Memory Computing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Darwin3: a large-scale neuromorphic chip with a novel ISA and on-chip learning.National science review · 2024Article
- Energy-Efficient Neuromorphic Architectures for Nuclear Radiation Detection Applications.Sensors (Basel, Switzerland) · 2024Article
- Deep learning applications in protein crystallography.Acta crystallographica. Section A, Foundations and advances · 2024Review
- An all integer-based spiking neural network with dynamic threshold adaptation.Frontiers in neuroscience · 2024Article
- WOScientific reports · 2023Article
- Hybrid Deep Learning Techniques for Securing Bioluminescent Interfaces in Internet of Bio Nano Things.Sensors (Basel, Switzerland) · 2023Article
- Quantum-noise-limited optical neural networks operating at a few quanta per activation.Research square · 2023Article
48 more citing papers are in PubMed but not listed here.
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Authors and funding
16 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep networks are now able to achieve human-level performance on a broad spectrum of recognition tasks. Independently, neuromorphic computing has now demonstrated unprecedented energy-efficiency through a new chip architecture based on spiking neurons, low precision synapses, and a scalable communication network. Here, we demonstrate that neuromorphic computing, despite its novel architectural primitives, can implement deep convolution networks that (i) approach state-of-the-art classification accuracy across eight standard datasets encompassing vision and speech, (ii) perform inference while preserving the hardware's underlying energy-efficiency and high throughput, running on the aforementioned datasets at between 1,200 and 2,600 frames/s and using between 25 and 275 mW (effectively >6,000 frames/s per Watt), and (iii) can be specified and trained using backpropagation with the same ease-of-use as contemporary deep learning. This approach allows the algorithmic power of deep learning to be merged with the efficiency of neuromorphic processors, bringing the promise of embedded, intelligent, brain-inspired computing one step closer.
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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.