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
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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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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
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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
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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
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Short-term synaptic plasticity in emerging devices for neuromorphic computing
iScience, 2023 Summary Neuromorphic computing is a promising computing paradigm toward building next-generation artificial intelligence machines, in which diverse types of synaptic plasticity play an active role in information processing.
Chao Li +9 more
semanticscholar +1 more source
Perspective: Organic electronic materials and devices for neuromorphic engineering
, 2018 Neuromorphic computing and engineering has been the focus of intense research efforts that have been intensified recently by the mutation of Information and Communication Technologies (ICT). In fact, new computing solutions and new hardware platforms are
Alibart, Fabien +2 more
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Stochastic Memristive Devices for Computing and Neuromorphic Applications
, 2013 Nanoscale resistive switching devices (memristive devices or memristors) have been studied for a number of applications ranging from non-volatile memory, logic to neuromorphic systems.
Choi, Shinhyun +4 more
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The Impact of On-chip Communication on Memory Technologies for Neuromorphic Systems
, 2018 Emergent nanoscale non-volatile memory technologies with high integration density offer a promising solution to overcome the scalability limitations of CMOS-based neural networks architectures, by efficiently exhibiting the key principle of neural ...
Manohar, Rajit, Moradi, Saber
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