Results 31 to 40 of about 28,339 (330)
Probing switching mechanism of memristor for neuromorphic computing
Nano Express, 2023 In recent, neuromorphic computing has been proposed to simulate the human brain system to overcome bottlenecks of the von Neumann architecture. Memristors, considered emerging memory devices, can be used to simulate synapses and neurons, which are the ...
Zhe Yang +5 more
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On-chip Few-shot Learning with Surrogate Gradient Descent on a Neuromorphic Processor [PDF]
, 2019 Recent work suggests that synaptic plasticity dynamics in biological models of neurons and neuromorphic hardware are compatible with gradient-based learning (Neftci et al., 2019).
Neftci, Emre +3 more
core +2 more sources
Introducing ‘Neuromorphic Computing and Engineering’
Neuromorphic Computing and Engineering, 2021 Abstract The standard nature of computing is currently being challenged by a range of problems that start to hinder technological progress. One of the strategies being proposed to address some of these problems is to develop novel brain-inspired processing methods and technologies, and apply them to a wide range of application ...
openaire +2 more sources
PyCARL: A PyNN Interface for Hardware-Software Co-Simulation of Spiking Neural Network
, 2020 We present PyCARL, a PyNN-based common Python programming interface for hardware-software co-simulation of spiking neural network (SNN). Through PyCARL, we make the following two key contributions.
Adiraju, Prathyusha +6 more
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Direct Feedback Alignment with Sparse Connections for Local Learning [PDF]
, 2019 Recent advances in deep neural networks (DNNs) owe their success to training algorithms that use backpropagation and gradient-descent. Backpropagation, while highly effective on von Neumann architectures, becomes inefficient when scaling to large ...
Crafton, Brian +3 more
core +2 more sources
Integrated Neuromorphic Photonics: Synapses, Neurons, and Neural Networks
Advanced Photonics Research, 2021 Ever‐growing demands of bandwidth, computing speed, and power consumption are now accelerating the transformation of computing research, as work‐at‐home becomes a new normal.
Xuhan Guo +3 more
doaj +1 more source
Recent advances in neuromorphic transistors for artificial perception applications
Science and Technology of Advanced Materials, 2023 Conventional von Neumann architecture is insufficient in establishing artificial intelligence (AI) in terms of energy efficiency, computing in memory and dynamic learning.
Wei Sheng Wang, Li Qiang Zhu
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Neuro-memristive Circuits for Edge Computing: A review
, 2018 The volume, veracity, variability, and velocity of data produced from the ever-increasing network of sensors connected to Internet pose challenges for power management, scalability, and sustainability of cloud computing infrastructure.
Chua, Leon O. +2 more
core +1 more source
Scalable network emulation on analog neuromorphic hardware
Frontiers in NeuroscienceWe present a novel software feature for the BrainScaleS-2 accelerated neuromorphic platform that facilitates the partitioned emulation of large-scale spiking neural networks.
Elias Arnold +6 more
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Convolutional Networks for Fast, Energy-Efficient Neuromorphic Computing
, 2016 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 ...
Amir, Arnon +15 more
core +1 more source