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Different from training common neural networks (NNs) for inference on general-purpose processors, the development of NNs for neuromorphic chips is usually faced with a number of hardware-specific restrictions. This paper proposes a systematic methodology to address the challenge. It can transform an existing trained, unrestricted NN (usually for software execution substrate) into an equivalent network...
Spiking Neural Networks (SNNs) are the third generation of artificial neural networks that closely mimic the time encoding and information processing aspects of the human brain. It has been postulated that these networks are more efficient for realizing cognitive computing systems compared to second generation networks that are widely used in machine learning algorithms today. In this paper, we review...
Over the last half century, the device community was guided by two quintessential laws that set the roadmap for device work: (1) Moore's law that provided the commercial push to double device count in a cadence of approximately two years and (2) Dennard's scaling laws that provided the physics to do just that. These driving forces slowing down due to power constraints. In fact, the clock frequency...
An ongoing challenge in neuromorphic computing is to devise general and computationally efficient models of inference and learning which are compatible with the spatial and temporal constraints of the brain. The gradient descent back-propagation rule is a powerful algorithm that is ubiquitous in deep learning, but it relies on the immediate availability of network-wide information stored with high-precision...
We present an ensemble approach for implementing Spiking Neural Networks (SNNs) with on-line unsupervised learning, well-suited for robust and energy-efficient design of neuromorphic computing systems for pattern recognition tasks. Inspired from the collective neuronal activity observed in the visual cortex, the proposed EnsembleSNN architecture involves multiple simple SNNs or ensembles acting in...
In the paper, we demonstrate a neuromorphic cognitive computing approach for Network Intrusion Detection System (IDS) for cyber security using Deep Learning (DL). The algorithmic power of DL has been merged with fast and extremely power efficient neuromorphic processors for cyber security. In this implementation, the data has been numerical encoded to train with un-supervised deep learning techniques...
With the end of Dennard scaling and the consequent slow-down of Moore's law, researchers are looking to exploit new device, circuit and architectural concepts to build future systems that are exponentially more capable than the systems of today. One promising approach is Neuromorphic computing based on Non-Von Neumann architectures, that could achieve orders of magnitude performance improvements over...
Neuromorphic computing systems are under heavy investigation as a potential substitute for the traditional von Neumann systems in high-speed low-power applications. Recently, memristor crossbar arrays were utilized in realizing spiking-based neuromorphic system, where memristor conductance values correspond to synaptic weights. Most of these systems are composed of a single crossbar layer, in which...
In this paper, an approach for increasing the sustainability of inverter-based memristive neuromorphic circuits in the presence of process variation is presented. The approach works based on extracting the impact of process variations on the neurons characteristics during the test phase through a proposed algorithm. In this method, first, some combinations of inputs and weights (based on the neuromorphic...
Brain inspired neuromorphic computing has demonstrated remarkable advantages over traditional von Neumann architecture for its high energy efficiency and parallel data processing. However, the limited resolution of synaptic weights degrades system accuracy and thus impedes the use of neuromorphic systems. In this work, we propose three orthogonal methods to learn synapses with one-level precision,...
At learning tasks where humans typically outperform computers, neuromorphic learning machines can have potential advantages in learning in terms of power and complexity compared to mainstream technologies. Here, we present Synaptic Sampling Machines (S2M), a class of stochastic neural networks that use stochasticity at the connections (synapses) to implement energy efficient semi- and unsupervised...
The recently emerged research on “neuromorphic computing”, which stands for hardware acceleration of brain-inspired computing, has become one of the most active research areas in computer engineering. In this invited paper, we start with a background introduction of neuromorphic computing, followed by some examples of hardware acceleration schemes of learning and neural network algorithms on emerging...
In recent years the field of neuromorphic low-power systems gained significant momentum, spurring brain-inspired hardware systems which operate on principles that are fundamentally different from standard digital computers and thereby consume orders of magnitude less power. However, their wider use is still hindered by the lack of algorithms that can harness the strengths of such architectures. While...
Reservoir Computing is an attractive paradigm of recurrent neural network architecture, due to the ease of training and existing neuromorphic implementations. Successively applied on speech recognition and time series forecasting, few works have so far studied the behavior of such networks on computer vision tasks. Therefore we decided to investigate the ability of Echo State Networks to classify...
We present an approach to constructing a neuromorphic device that responds to language input by producing neuron spikes in proportion to the strength of the appropriate positive or negative emotional response. Specifically, we perform a fine-grained sentiment analysis task with implementations on two different systems: one using conventional spiking neural network (SNN) simulators and the other one...
There are a number of mature ways to train various kinds of ANNs (artificial neural networks), including the BP (back propagation) based algorithm and so on. These training procedures are usually carried out on some GPU-enabled machine(s); 16-/32-bit-width floating point numbers are used as the NN parameters, without any limitation on the maximum fan-in/fan-out of a single neuron or on the type of...
Discovery of memristor opened a new era of the research on universal memory thanks to many attractive properties demonstrated by this emerging device. In this paper, we switch our research focus to neuromorphic computing, which, same as memory technology, significantly benefits from the technical advances of memristor. Particularly, we present the implementation of cortical processor augmented with...
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