Mohammad Bavandpour - Academia.edu (original) (raw)

Papers by Mohammad Bavandpour

This paper presents a set of reconfigurable analog implementations of piecewise linear spiking ne... more This paper presents a set of reconfigurable analog implementations of piecewise linear spiking neuron models using second generation current conveyor (CCII) building blocks. With the same topology and circuit elements, without W/L modification which is impossible after circuit fabrication, these circuits can produce different behaviors, similar to the biological neurons, both for a single neuron as well as a network of neurons just by tuning reference current and voltage sources. The models are investigated, in terms of analog implementation feasibility and costs, targeting large scale hardware implementations. Results show that, in order to gain the best performance, area and accuracy; these models can be compromised. Simulation results are presented for different neuron behaviors with CMOS 350 nm technology.

UKSIM, 2012

Since biological neural systems contain big number of neurons working in parallel, simulation o... more Since biological neural systems contain big number of
neurons working in parallel, simulation of such dynamic system
is a real challenge. The main objective of this paper is to speed
up the simulation performance of SystemC designs at the RTL
abstraction level using the high degree of parallelism afforded
by graphics processors (GPUs) for large scale SNN with
proposed structure in pattern classification field. Simulation
results show 100 times speedup for the proposed SNN structure
on the GPU compared with the CPU version. In addition, CPU
memory has problems when trained for more than 120K cells
but GPU can simulate up to 40 million neurons.

Ieee Transactions on Neural Networks and Learning Systems, 2015

This paper presents a modified astrocyte model that allows a convenient digital implementation. T... more This paper presents a modified astrocyte model that allows a convenient digital implementation. This model is aimed at reproducing relevant biological astrocyte behaviors, which provide appropriate feedback control in regulating neuronal activities in the central nervous system. Accordingly, we investigate the feasibility of a digital implementation for a single astrocyte and a biological neuronal network model constructed by connecting two limit-cycle Hopf oscillators to an implementation of the proposed astrocyte model using oscillator-astrocyte interactions with weak coupling. Hardware synthesis, physical implementation on field-programmable gate array, and theoretical analysis confirm that the proposed astrocyte model, with considerably low hardware overhead, can mimic biological astrocyte model behaviors, resulting in desynchronization of the two coupled limit-cycle oscillators.

Frontiers in Neuroscience, 2015

This study firstly presents (i) a novel general cellular mapping scheme for two dimensional neuro... more This study firstly presents (i) a novel general cellular mapping scheme for two dimensional neuromorphic dynamical systems such as bio-inspired neuron models, and (ii) an efficient mixed analog-digital circuit, which can be conveniently implemented on a hybrid memristor-crossbar/CMOS platform, for hardware implementation of the scheme. This approach employs 4n memristors and no switch for implementing an n-cell system in comparison with 2n (2) memristors and 2n switches of a Cellular Memristive Dynamical System (CMDS). Moreover, this approach allows for dynamical variables with both analog and one-hot digital values opening a wide range of choices for interconnections and networking schemes. Dynamical response analyses show that this circuit exhibits various responses based on the underlying bifurcation scenarios which determine the main characteristics of the neuromorphic dynamical systems. Due to high programmability of the circuit, it can be applied to a variety of learning systems, real-time applications, and analytically indescribable dynamical systems. We simulate the FitzHugh-Nagumo (FHN), Adaptive Exponential (AdEx) integrate and fire, and Izhikevich neuron models on our platform, and investigate the dynamical behaviors of these circuits as case studies. Moreover, error analysis shows that our approach is suitably accurate. We also develop a simple hardware prototype for experimental demonstration of our approach.

2013 21st Iranian Conference on Electrical Engineering (ICEE), 2013

ABSTRACT Process variation has an increasingly dramatic effect on delay and power as process geom... more ABSTRACT Process variation has an increasingly dramatic effect on delay and power as process geometries shrink. Even if the amount of variation remains the same as in previous generations, it accounts for a greater percentage of process geometries as they get smaller. So an accurate prediction of path delay and power variability for real digital circuits in the current technologies is very important; however, its main drawback is the high runtime cost. In this paper, we present a new fast EDA tool which accelerates Monte Carlo based statistical static timing analysis (SSTA) for complex digital circuit. Parallel platforms like Message Passing Interface and POSIX® Threads and also the GPU-based CUDA platform suggests a natural fit for this analysis. So using these platforms, Monte Carlo based SSTA for complex digital circuits at 32, 45 and 65 nm has been performed. and of the pin-to-output delay and power distributions for all basic gates are extracted using a memory lookup from Hspice and then the results are extended to the complex digital circuit in a hierarchal manner on the parallel platforms. Results show that the GPU-based platform has the highest performance (speedup of 19x). The correctness of the Monte Carlo based SSTA implemented on a GPU has been verified by comparing its results with a CPU based implementation.

In this paper we study the neural dynamics and bifurcation analysis of easily implementable piece... more In this paper we study the neural dynamics and bifurcation analysis of easily implementable piecewise linear (PWL) spiking neuron models with membrane potential vari-ables, input current, recovery variables, and parameters describ-ing timescales. Analyses reveal that the models can reproduce six (all) kinds of bifurcation phenomena that are observed in standard biological neuron models. Moreover, these bifurcations are confirmed by time domain simulations, and along with the different phase plane geometries, these qualitative analyses provide explicit tool for the interpretation of different spiking patterns, and to guide parameter selection in PWL neuron models.

Microelectronics Journal, 2014

This study proposes a spiking neuro-fuzzy clustering system based on a novel spike encoding schem... more This study proposes a spiking neuro-fuzzy clustering system based on a novel spike encoding scheme and a compatible learning algorithm. In this system, we utilize an analog to binary encoding scheme that properly maps the concept of "distance" in multi-dimensional analog spaces to the concept of "dissimilarity" of binary bits in the equivalent binary spaces. When this scheme is combined with a novel binary to spike encoding scheme and a proper learning algorithm is applied, a powerful clustering algorithm is produced. This algorithm creates flexible fuzzy clusters in its analog input space and modifies their shapes to different convex shapes during the learning process. This system has plausible biological support due to its spike-based learning mechanism, its Quasi Spike Time Dependent Plasticity learning policy and its brain-like fuzzy clustering performance. Moreover, this neuro-fuzzy system is fully implementable on the hybrid memristor-crossbar/CMOS platform. The resultant circuit was simulated on one clustering task carried out in the binary input space on the Simon Lucas handwritten dataset and another clustering task carried out in the analog input space on Fisher's Iris standard dataset. The results show that it attained a higher clustering rate in comparison with other algorithms such as the Self Organizing Map, K-mean and the Spiking Radial Basis Function. The circuit was also successfully simulated on an image segmentation task and some clustering tasks performed in noisy spaces with various cluster sizes. Furthermore, the circuit variability analysis shows that device and signal variations up to 20% had no significant impact on the circuit's clustering performance, so the system is sufficiently immune to different variations due to its fuzzy nature. (M. Bavandpour). Please cite this article as: M. Bavandpour, et al., Spiking neuro-fuzzy clustering system and its memristor crossbar based implementation, Microelectron. J (2014), http://dx.Microelectronics Journal ∎ (∎∎∎∎) ∎∎∎-∎∎∎ Please cite this article as: M. Bavandpour, et al., Spiking neuro-fuzzy clustering system and its memristor crossbar based implementation, Microelectron. J (2014), http://dx.

International Journal of Bifurcation and Chaos, 2014

ABSTRACT This study presents a cellular-based mapping for a special class of dynamical systems fo... more ABSTRACT This study presents a cellular-based mapping for a special class of dynamical systems for embedding neuron models, by exploiting an efficient memristor crossbar-based circuit for its implementation. The resultant reconfigurable memristive dynamical circuit exhibits various bifurcation phenomena, and responses that are characteristic of dynamical systems. High programmability of the circuit enables it to be applied to real-time applications, learning systems, and analytically indescribable dynamical systems. Moreover, its efficient implementation platform makes it an appropriate choice for on-chip applications and prostheses. We apply this method to the Izhikevich, and FitzHugh–Nagumo neuron models as case studies, and investigate the dynamical behaviors of these circuits.

IEEE Transactions on Neural Networks and Learning Systems, 2015

ICEE 2012 - 20th Iranian Conference on Electrical Engineering, 2012

ABSTRACT In this paper we demonstrate a dynamical system simulator that runs on a single GPU. The... more ABSTRACT In this paper we demonstrate a dynamical system simulator that runs on a single GPU. The model (running on an NVIDIA GT325M with I GB of memory) is up to 50 times faster than a CPU version when more than 10 million adaptive Hopf oscillators have been simulated. The simulation shows that the oscillators tune to the correct frequencies for both discrete and continuous spectra. Due to its dynamic nature the system is also capable to track non-stationary spectra. With the help of this model the frequency spectrum of an ECG signal (as a non-stationary signal) obtained and was showed that frequency domain representation of signal (i.e. FFT) is the same as one MATLAB generates.

2014 22nd Iranian Conference on Electrical Engineering (ICEE), 2014

ABSTRACT In this paper we study the neural dynamics and bifurcation analysis of easily implementa... more ABSTRACT In this paper we study the neural dynamics and bifurcation analysis of easily implementable piecewise linear (PWL) spiking neuron models with membrane potential vari-ables, input current, recovery variables, and parameters describ-ing timescales. Analyses reveal that the models can reproduce six (all) kinds of bifurcation phenomena that are observed in standard biological neuron models. Moreover, these bifurcations are confirmed by time domain simulations, and along with the different phase plane geometries, these qualitative analyses provide explicit tool for the interpretation of different spiking patterns, and to guide parameter selection in PWL neuron models.

Since biological neural systems contain big number of neurons working in parallel, simulation of ... more Since biological neural systems contain big number of neurons working in parallel, simulation of such dynamic system is a real challenge. The main objective of this paper is to speed up the simulation performance of SystemC designs at the RTL abstraction level using the high degree of parallelism afforded by graphics processors (GPUs) for large scale SNN with proposed structure in pattern classification field. Simulation results show 100 times speedup for the proposed SNN structure on the GPU compared with the CPU version. In addition, CPU memory has problems when trained for more than 120K cells but GPU can simulate up to 40 million neurons.

20th Iranian Conference on Electrical Engineering (ICEE2012), 2012

Circuits, Systems, and Signal Processing, 2014

ABSTRACT This paper presents a digital hardware implementation of a frequency adaptive Hopf oscil... more ABSTRACT This paper presents a digital hardware implementation of a frequency adaptive Hopf oscillator along with investigation on systematic behavior when they are coupled in a population. The mathematical models of the oscillator are introduced and compared in sense of dynamical behavior by using system-level simulations based on which a piecewise-linear model is developed. It is shown that the model is capable to be implemented digitally with high efﬁciency. Behavior of the oscillators in different network structures to be used for dynamic Fourier analysis is studied and a structure with more precise operation which is also more efﬁcient for FPGA-based implementation is implemented. Conceptual block-diagram and a high level representation for this network structure are shown where design process and synthesis are explained based on which physical implementation is demonstrated and tested.

there has been a strong push recently to examine biological scale simulations of neuromorphic alg... more there has been a strong push recently to examine biological scale simulations of neuromorphic algorithms to achieve stronger inference capabilities. This paper presents a set of piecewise linear spiking neuron models, which can reproduce different behaviors, similar to the biological neuron, both for a single neuron as well as a network of neurons. The proposed models are investigated, in terms of digital implementation feasibility and costs, targeting large scale hardware implementation.