7,086 research outputs found
Memory and information processing in neuromorphic systems
A striking difference between brain-inspired neuromorphic processors and
current von Neumann processors architectures is the way in which memory and
processing is organized. As Information and Communication Technologies continue
to address the need for increased computational power through the increase of
cores within a digital processor, neuromorphic engineers and scientists can
complement this need by building processor architectures where memory is
distributed with the processing. In this paper we present a survey of
brain-inspired processor architectures that support models of cortical networks
and deep neural networks. These architectures range from serial clocked
implementations of multi-neuron systems to massively parallel asynchronous ones
and from purely digital systems to mixed analog/digital systems which implement
more biological-like models of neurons and synapses together with a suite of
adaptation and learning mechanisms analogous to the ones found in biological
nervous systems. We describe the advantages of the different approaches being
pursued and present the challenges that need to be addressed for building
artificial neural processing systems that can display the richness of behaviors
seen in biological systems.Comment: Submitted to Proceedings of IEEE, review of recently proposed
neuromorphic computing platforms and system
Born to learn: The inspiration, progress, and future of evolved plastic artificial neural networks
Biological plastic neural networks are systems of extraordinary computational
capabilities shaped by evolution, development, and lifetime learning. The
interplay of these elements leads to the emergence of adaptive behavior and
intelligence. Inspired by such intricate natural phenomena, Evolved Plastic
Artificial Neural Networks (EPANNs) use simulated evolution in-silico to breed
plastic neural networks with a large variety of dynamics, architectures, and
plasticity rules: these artificial systems are composed of inputs, outputs, and
plastic components that change in response to experiences in an environment.
These systems may autonomously discover novel adaptive algorithms, and lead to
hypotheses on the emergence of biological adaptation. EPANNs have seen
considerable progress over the last two decades. Current scientific and
technological advances in artificial neural networks are now setting the
conditions for radically new approaches and results. In particular, the
limitations of hand-designed networks could be overcome by more flexible and
innovative solutions. This paper brings together a variety of inspiring ideas
that define the field of EPANNs. The main methods and results are reviewed.
Finally, new opportunities and developments are presented
Adaptive motor control and learning in a spiking neural network realised on a mixed-signal neuromorphic processor
Neuromorphic computing is a new paradigm for design of both the computing
hardware and algorithms inspired by biological neural networks. The event-based
nature and the inherent parallelism make neuromorphic computing a promising
paradigm for building efficient neural network based architectures for control
of fast and agile robots. In this paper, we present a spiking neural network
architecture that uses sensory feedback to control rotational velocity of a
robotic vehicle. When the velocity reaches the target value, the mapping from
the target velocity of the vehicle to the correct motor command, both
represented in the spiking neural network on the neuromorphic device, is
autonomously stored on the device using on-chip plastic synaptic weights. We
validate the controller using a wheel motor of a miniature mobile vehicle and
inertia measurement unit as the sensory feedback and demonstrate online
learning of a simple 'inverse model' in a two-layer spiking neural network on
the neuromorphic chip. The prototype neuromorphic device that features 256
spiking neurons allows us to realise a simple proof of concept architecture for
the purely neuromorphic motor control and learning. The architecture can be
easily scaled-up if a larger neuromorphic device is available.Comment: 6+1 pages, 4 figures, will appear in one of the Robotics conference
On-chip Few-shot Learning with Surrogate Gradient Descent on a Neuromorphic Processor
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). Gradient-based learning requires iterating
several times over a dataset, which is both time-consuming and constrains the
training samples to be independently and identically distributed. This is
incompatible with learning systems that do not have boundaries between training
and inference, such as in neuromorphic hardware. One approach to overcome these
constraints is transfer learning, where a portion of the network is pre-trained
and mapped into hardware and the remaining portion is trained online. Transfer
learning has the advantage that pre-training can be accelerated offline if the
task domain is known, and few samples of each class are sufficient for learning
the target task at reasonable accuracies. Here, we demonstrate on-line
surrogate gradient few-shot learning on Intel's Loihi neuromorphic research
processor using features pre-trained with spike-based gradient
backpropagation-through-time. Our experimental results show that the Loihi chip
can learn gestures online using a small number of shots and achieve results
that are comparable to the models simulated on a conventional processor
Conversion of Artificial Recurrent Neural Networks to Spiking Neural Networks for Low-power Neuromorphic Hardware
In recent years the field of neuromorphic low-power systems that consume
orders of magnitude less power gained significant momentum. However, their
wider use is still hindered by the lack of algorithms that can harness the
strengths of such architectures. While neuromorphic adaptations of
representation learning algorithms are now emerging, efficient processing of
temporal sequences or variable length-inputs remain difficult. Recurrent neural
networks (RNN) are widely used in machine learning to solve a variety of
sequence learning tasks. In this work we present a train-and-constrain
methodology that enables the mapping of machine learned (Elman) RNNs on a
substrate of spiking neurons, while being compatible with the capabilities of
current and near-future neuromorphic systems. This "train-and-constrain" method
consists of first training RNNs using backpropagation through time, then
discretizing the weights and finally converting them to spiking RNNs by
matching the responses of artificial neurons with those of the spiking neurons.
We demonstrate our approach by mapping a natural language processing task
(question classification), where we demonstrate the entire mapping process of
the recurrent layer of the network on IBM's Neurosynaptic System "TrueNorth", a
spike-based digital neuromorphic hardware architecture. TrueNorth imposes
specific constraints on connectivity, neural and synaptic parameters. To
satisfy these constraints, it was necessary to discretize the synaptic weights
and neural activities to 16 levels, and to limit fan-in to 64 inputs. We find
that short synaptic delays are sufficient to implement the dynamical (temporal)
aspect of the RNN in the question classification task. The hardware-constrained
model achieved 74% accuracy in question classification while using less than
0.025% of the cores on one TrueNorth chip, resulting in an estimated power
consumption of ~17 uW
Neuroinspired unsupervised learning and pruning with subquantum CBRAM arrays.
Resistive RAM crossbar arrays offer an attractive solution to minimize off-chip data transfer and parallelize on-chip computations for neural networks. Here, we report a hardware/software co-design approach based on low energy subquantum conductive bridging RAM (CBRAM®) devices and a network pruning technique to reduce network level energy consumption. First, we demonstrate low energy subquantum CBRAM devices exhibiting gradual switching characteristics important for implementing weight updates in hardware during unsupervised learning. Then we develop a network pruning algorithm that can be employed during training, different from previous network pruning approaches applied for inference only. Using a 512 kbit subquantum CBRAM array, we experimentally demonstrate high recognition accuracy on the MNIST dataset for digital implementation of unsupervised learning. Our hardware/software co-design approach can pave the way towards resistive memory based neuro-inspired systems that can autonomously learn and process information in power-limited settings
Real time unsupervised learning of visual stimuli in neuromorphic VLSI systems
Neuromorphic chips embody computational principles operating in the nervous
system, into microelectronic devices. In this domain it is important to
identify computational primitives that theory and experiments suggest as
generic and reusable cognitive elements. One such element is provided by
attractor dynamics in recurrent networks. Point attractors are equilibrium
states of the dynamics (up to fluctuations), determined by the synaptic
structure of the network; a `basin' of attraction comprises all initial states
leading to a given attractor upon relaxation, hence making attractor dynamics
suitable to implement robust associative memory. The initial network state is
dictated by the stimulus, and relaxation to the attractor state implements the
retrieval of the corresponding memorized prototypical pattern. In a previous
work we demonstrated that a neuromorphic recurrent network of spiking neurons
and suitably chosen, fixed synapses supports attractor dynamics. Here we focus
on learning: activating on-chip synaptic plasticity and using a theory-driven
strategy for choosing network parameters, we show that autonomous learning,
following repeated presentation of simple visual stimuli, shapes a synaptic
connectivity supporting stimulus-selective attractors. Associative memory
develops on chip as the result of the coupled stimulus-driven neural activity
and ensuing synaptic dynamics, with no artificial separation between learning
and retrieval phases.Comment: submitted to Scientific Repor
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