Investigation of Synapto-dendritic Kernel Adapting Neuron models and their use in spiking neuromorphic architectures

The motivation for this thesis is idea that abstract, adaptive, hardware efficient, inter-neuronal transfer functions (or kernels) which carry information in the form of postsynaptic membrane potentials, are the most important (and erstwhile missing) element in neuromorphic implementations of Spiking Neural Networks (SNN). In the absence of such abstract kernels, spiking neuromorphic systems must realize very large numbers of synapses and their associated connectivity. The resultant hardware and bandwidth limitations create difficult tradeoffs which diminish the usefulness of such systems. In this thesis a novel model of spiking neurons is proposed. The proposed Synapto-dendritic Kernel Adapting Neuron (SKAN) uses the adaptation of their synapto-dendritic kernels in conjunction with an adaptive threshold to perform unsupervised learning and inference on spatio-temporal spike patterns. The hardware and connectivity requirements of the neuron model are minimized through the use of simple accumulator-based kernels as well as through the use of timing information to perform a winner take all operation between the neurons. The learning and inference operations of SKAN are characterized and shown to be robust across a range of noise environments. Next, the SKAN model is augmented with a simplified hardware-efficient model of Spike Timing Dependent Plasticity (STDP). In biology STDP is the mechanism which allows neurons to learn spatio-temporal spike patterns. However when the proposed SKAN model is augmented with a simplified STDP rule, where the synaptic kernel is used as a binary flag that enable synaptic potentiation, the result is a synaptic encoding of afferent Signal to Noise Ratio (SNR). In this combined model the neuron not only learns the target spatio-temporal spike patterns but also weighs each channel independently according to its signal to noise ratio. Additionally a novel approach is presented to achieving homeostatic plasticity in digital hardware which reduces hardware cost by eliminating the need for multipliers. Finally the behavior and potential utility of this combined model is investigated in a range of noise conditions and the digital hardware resource utilization of SKAN and SKAN + STDP is detailed using Field Programmable Gate Arrays (FPGA)

High speed event-based visual processing in the presence of noise

Standard machine vision approaches are challenged in applications where large amounts of noisy temporal data must be processed in real-time. This work aims to develop neuromorphic event-based processing systems for such challenging, high-noise environments. The novel event-based application-focused algorithms developed are primarily designed for implementation in digital neuromorphic hardware with a focus on noise robustness, ease of implementation, operationally useful ancillary signals and processing speed in embedded systems

Reliability and Cost Model of P.M. in A Component of an Electrical Distribution System Considering Ageing Mechanism

Application of Reliability Centered Maintenance (RCM) in a system results in a decrease in component failure rates and as such improvement in the system reliability. One of the major subjects of the RCM is focused on the Online and Offline Preventive Maintenance (OPM and FPM which together will be denoted by OFPM) of the components which repairing the component needs or doesn’t need to stop the mission carrying out by it. The RCM is classified as a preventive maintenance policy and has significant contribution in practical applications. However, little research has been devoted to modeling the online and offline Preventive Maintenance. This research assumes that the component failure rate will be improved if the OFPM is performed for a long period of time as a part of an RCM program. Application of an OFPM program could cause the component set at least to “as bad as old state but cannot reach the “as good as new” state. The emphasis of this research is to model the OFPM for critical components or any equipment with critical failure in a system. The proposed model is based on the concept of PM and improvement factor of reliability in a system with critical components which their failure could cause a failure in the system (first-order cut- sets).DOI:http://dx.doi.org/10.11591/ijece.v4i2.551

An optimised deep spiking neural network architecture without gradients

We present an end-to-end trainable modular event-driven neural architecture that uses local synaptic and threshold adaptation rules to perform transformations between arbitrary spatio-temporal spike patterns. The architecture represents a highly abstracted model of existing Spiking Neural Network (SNN) architectures. The proposed Optimized Deep Event-driven Spiking neural network Architecture (ODESA) can simultaneously learn hierarchical spatio-temporal features at multiple arbitrary time scales. ODESA performs online learning without the use of error back-propagation or the calculation of gradients. Through the use of simple local adaptive selection thresholds at each node, the network rapidly learns to appropriately allocate its neuronal resources at each layer for any given problem without using a real-valued error measure. These adaptive selection thresholds are the central feature of ODESA, ensuring network stability and remarkable robustness to noise as well as to the selection of initial system parameters. Network activations are inherently sparse due to a hard Winner-Take-All (WTA) constraint at each layer. We evaluate the architecture on existing spatio-temporal datasets, including the spike-encoded IRIS and TIDIGITS datasets, as well as a novel set of tasks based on International Morse Code that we created. These tests demonstrate the hierarchical spatio-temporal learning capabilities of ODESA. Through these tests, we demonstrate ODESA can optimally solve practical and highly challenging hierarchical spatio-temporal learning tasks with the minimum possible number of computing nodes.Comment: 18 pages, 6 figure

Bulk-boundary and RPS Thermodynamics from Topology perspective

In this article, we investigate the bulk-boundary and restricted phase space (RPS) thermodynamics of Rissner-Nordstr\"om (R-N) AdS and 6-dimensional charged Gauss-Bonnet AdS black holes. Also, we examine the topological characteristics of the considered black holes and compare them with the extended thermodynamics results. In fact, we have found that the topological behavior of the bulk-boundary thermodynamics is the same as that of the extended thermodynamics, whereas the RPS thermodynamics exhibits a distinct behavior. We also demonstrate that within the RPS formalism, there is only one critical point with a topological charge of +1 $(Q_t=+1)$. Additionally, for RPS formalism, the inclusion of higher derivative curvature terms in the form of Gauss-Bonnet gravity does not alter the topological classification of critical points in charged AdS black holes.Comment: 17 pages, 6 figures, 1 Tabl

Bardeen Black Hole Thermodynamics from Topological Perspective

In this paper, we use the generalized off-shell Helmholtz free energy method to explore the thermodynamic properties of Bardeen black holes (BD BHs) from a topological perspective based on Duan's topological current $\phi$-mapping. We consider various structures of BD BHs, including regular BD-AdS BHs, BD-AdS BHs in Kiselev's model of quintessence, BD BHs in massive gravity (MG), and BD BHs in 4D Einstein-Gauss-Bonnet (EGB) gravity. We demonstrate that these BHs have one topological classification (TC), i.e., TC is +1 for all BHs considered, and the addition of MG or GB terms, etc., does not change the topological numbers.Comment: 16 pages, 4 figures, 1 table, Accepted for publication in the Annals of Physic

Thermodynamic topology and photon spheres in the Hyperscaling violation black hole

It was shown that a standard ring of light can be imagined outside the event horizon for stationary rotating four-dimensional black holes with axial symmetry using the topological method. Based on this concept, in this paper, we investigate the topological charge and the conditions of existence of the photon sphere (PS) for a hyperscaling violation (HSV) black hole with various values of the parameters of this model. Then, after carrying out a detailed analysis, we show the conventional topological classes viz $Q=-1$ for the mentioned black hole and $Q=0$ for the naked singularities. Also, we propose a new topological class for naked singularities ($Q=+1$) with respect to $z>1$. Then, we will use two different methods, namely the temperature (Duan's topological current $\Phi$-mapping theory) and the generalized Helmholtz free energy method, to study the topological classes of our black hole. By considering the black hole mentioned, we discuss the critical and zero points (topological charges and topological numbers) for different parameters of hyperscaling violating black holes, such as ($z, \theta$) and other free parameters, and study their thermodynamic topology. We observe that for a given value of the parameters $z$, $\theta$, and other free parameters, there exist two total topological charges $(Q_{t}=-1, 0)$ for the $T$ method and two total topological numbers $(W=+1)$ for the generalized Helmholtz free energy method. Additionally, we summarize the results for each study as photon sphere, temperature, and generalized Helmholtz free energy in some figures and tables. Finally, we compare our findings with other related studies in the literature.Comment: 38 pages, 21 figures and 13 table

An optimized multi-layer spiking neural network implementation in FPGA without multipliers

This paper presents an expansion and evaluation of the hardware architecture for the Optimized Deep Event-driven Spiking Neural Network Architecture (ODESA). ODESA is a state-of-the-art, event-driven multi-layer Spiking Neural Network (SNN) architecture that offers an end-to-end, online, and local supervised training method. In previous work, ODESA was successfully implemented on Field-Programmable Gate Array (FPGA) hardware, showcasing its effectiveness in resource-constrained hardware environments. Building upon the previous implementation, this research focuses on optimizing the ODESA network hardware by introducing a novel approach. Specifically, we propose substituting the dot product multipliers in the Neurons with a low-cost shift-register design. This optimization strategy significantly reduces the hardware resources required for implementing a neuron, thereby enabling more complex SNNs to be accommodated within a single FPGA. Additionally, this optimization results in a reduction in power consumption, further enhancing the practicality and efficiency of the hardware implementation. To evaluate the effectiveness of the proposed optimization, extensive experiments and measurements were conducted. The results demonstrate the successful reduction in hardware resource utilization while maintaining the network's functionality and performance. Moreover, the power consumption reduction contributes to the overall energy efficiency of the hardware implementation