The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Modeling biological behaviour requires a multidisciplinary approach and very large computational resources. In this paper, we present an overview of our research work with regards to biological neuron network (NN) modeling, electrochemical reaction emulation for neurotransmitters and finite element modeling (FEM) of a very low pressure micropump for neurotransmitter concentration modulations. Three...
The expanding use of deep learning algorithms causes the demands for accelerating neural network (NN) signal processing. For the NN processing, in-memory computation is desired, in which expensive data transfer can be eliminated. In reflection of recently proposed binary neural networks (BNNs), which can reduce the computation resource and area requirements, we designed an in-memory BNN signal processor...
This paper proposes an optimized pedestrian and vehicle detection method based on deep learning technique. We optimize the convolutional neural network architecture by three mainly methods. The first one is the choice of the learning policy. The second one is to simplify the convolutional neural network architecture. The last one is careful choice of training samples. With limited loss of accuracy,...
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...
A spiking neuron and 3-terminal Resistive RAM (RRAM) model are proposed and simulated as a neural network. The system is analyzed as a complex network of spiking neurons connected by synapses to demonstrate a biologically-inspired associative memory. In recent years, Machine Learning and Artificial Intelligence have become popular fields due to readily available high performance computing systems...
Exploiting resource reusability and low precision in neural networks is a promising approach to achieve energy efficient computational platforms. This research presents two generalizable approaches to reuse resources in feed-forward neural networks and demonstrated on extreme learning machines. In the first approach, coalescing, a single stack of neuronal units perform both feature extraction and...
In this paper we present a memristive neuromorphic system for higher power and area efficiency. The system is based on a mixed signal approach considering the digital nature of the peripheral and control logics and the integration being analog. So, the system is connected digitally outside but the core is purely analog. This mixed signal approach provides the advantage of implementing neural networks...
In this paper, we present artificial neural network (ANN) models to predict hard and soft-responses of three configurations of arbiter based physical unclonable functions (PUFs): standard, feed-forward (FF) and modified feed-forward (MFF). The models are trained using data extracted from 32-stage arbiter PUF circuits fabricated using IBM 32 nm HKMG process. The contributions of this paper are two-fold...
Spiking Neural Networks offer low precision communication, robustness, and low power consumption and are attractive for autonomous applications. One of the well accepted learning rules for these networks is spike time dependent plasticity which is governed by the pre- and postsynaptic spike timings. To stabilize the plasticity and avoid saturation in these learning rules, synaptic normalization is...
In-Memory computing has drawn many attentions as a promising solution to reduce massive power hungry data traffic between computing and memory units, leading to significant improvement of entire system performance and energy efficiency. Emerging spintronic device based non-volatile memory is becoming a next-generation universal memory candidate due to its non-volatility, zero leakage power in un-accessed...
A CMOS synapse design is presented which can perform tunable asymmetric spike timing-dependent learning in asynchronous spiking neural networks. The overall design consists of three primary subcircuit blocks, and the operation of each is described. Pair-based Spike Timing-Dependent Plasticity (STDP) of the entire synapse is then demonstrated through simulation using the Cadence Virtuoso platform....
Emerging trend in neuromorphic implementation moves towards large-scale neuron array that processes large amount of input data. It presents a grand challenge in communication between neurons due to large number of connections, or synapses, which can be in the order of millions, leading to large power consumption, processing time and many physical wires. In this work, we propose a communication protocol...
In this paper, a novel neural network architecture is proposed which results in an area-efficient feed-forward network. These structures require high-resolution multipliers. In order to overcome this problem, a mixed-signal Multiplying Digital to Analog Converter (MDAC) architecture which employs Delta-Sigma Modulation (DSM) to encode the multiplication results into the time domain. The time-domain...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.