OSPEN: an open source platform for emulating neuromorphic hardware

International Journal of Reconfigurable and Embedded Systems (IJRES)
Vol. 12, No. 1, March 2023, pp. 1~8
ISSN: 2089-4864, DOI: 10.11591/ijres.v12.i1.pp1-8  1
Journal homepage: http://ijres.iaescore.com
OSPEN: an open source platform for emulating neuromorphic
hardware
Arfan Ghani1
, Thomas Dowrick2
, Liam J. McDaid3
1
School of Engineering, American University of Ras Al Khaimah, Ras Al Khaimah, United Arab Emirates
2
Departmen of Medical Physics, University College London, London, United Kingdom
3
School of Computing, Engineering and Intelligent Systems, University of Ulster, Londonderry, United Kingdom
Article Info ABSTRACT
Article history:
Received Mar 1, 2022
Revised Jun 2, 2022
Accepted Jul 16, 2022
This paper demonstrates a framework that entails a bottom-up approach to
accelerate research, development, and verification of neuro-inspired sensing
devices for real-life applications. Previous work in neuromorphic
engineering mostly considered application-specific designs which is a strong
limitation for researchers to develop novel applications and emulate the true
behaviour of neuro-inspired systems. Hence to enable the fully parallel
brain-like computations, this paper proposes a methodology where a spiking
neuron model was emulated in software and electronic circuits were then
implemented and characterized. The proposed approach offers a unique
perspective whereby experimental measurements taken from a fabricated
device allowing empirical models to be developed. This technique acts as a
bridge between the theoretical and practical aspects of neuro-inspired
devices. It is shown through software simulations and empirical modelling
that the proposed technique is capable of replicating neural dynamics and
post-synaptic potentials. Retrospectively, the proposed framework offers a
first step towards open-source neuro-inspired hardware for a range of
applications such as healthcare, applied machine learning and the internet of
things (IoT).
Keywords:
Artificial intelligence chips
Chip design
Neural computing
Open-source hardware
Silicon neurons
Spike response model
Spiking neurons
This is an open access article under the CC BY-SA license.
Corresponding Author:
Arfan Ghani
Department of Computer Science and Engineering, School of Engineering
American University of Ras al Khaimah
American University of Ras al Khaimah Road, Seih Al Araibi, Ras Al Khaimah, United Arab Emirates
Email: Ghani_786@yahoo.com
1. INTRODUCTION
With silicon pushed to its limits, we must look beyond conventional computing platforms. The brain
realizes a huge number of tasks through flexible and power-efficient networks of neurons. To enable brain-
like computations, a power-efficient neural fabric is needed to emulate the basic functionality of the human
brain. The task of neural hardware implementation is challenging due to a large number of processing nodes
(roughly 1011
) and rich interconnectivity (roughly 1015
) [1], [2]. Research work has recently focused on
models which exhibit more plausibility to the human neural system. Similarly, wearable devices and sensors
are commonly used for detecting abnormalities such as detection of seizures, patterns of movement, and
behaviour [1]. However, continuous sampling of input signals requires power, hence neuro-inspired
paradigms offer an alternative to the existing limitation of continuous signal measurement. The power aspect
becomes even more important when it comes to applications based on lightweight portable devices.
Biologically plausible spiking neural models such as the spike response model (SRM) are
particularly suitable in computational neuroscience because it is compute-efficient and captures most of the

 ISSN: 2089-4864
Int J Reconfigurable & Embedded Syst, Vol. 12, No. 1, March 2023: 1-8
2
biological dynamics [2]-[4]. Nonetheless, to efficiently emulate thousands of such neural entities on
hardware/software platforms is challenging. As one of the main features of the human neural system is its
parallel processing capability, during the past few years, significant advances have been made in techniques,
methodologies, and applications of neural-based hardware [5]-[12]. The brain realizes a huge number of tasks
such as adaptation, vision and recognition through flexible and power-efficient networks of neurons. It is
challenging to investigate an unconventional multidisciplinary area of neural engineering because of the
background knowledge required to understand biological principles and then apply them in computing-
related applications. Growing research in the area of low-power neural engineering and related areas has
triggered the interest to model such systems on electronic platforms [13]. The task of neural hardware
implementation requires an extremely large number of processing nodes, rich interconnectivity, and
adaptation mechanisms [14]. It is well understood that low-power devices with particular applications in
environmental sensing, healthcare and internet of things (IoTs) represent unprecedented challenges with the
growing need for trillions of interconnected devices, ubiquitous sensors, and actuators that are expected to be
interconnected. There are several challenges that the scientific community faces today such as compatible
architectures and platforms for such devices to talk to each other and actual hardware with battery-less
energy scavenging techniques, to name a few.
This paper offers a novel neuro-inspired platform that could potentially be used for research as well
as integrating several neuro-inspired applications such as healthcare electronics and integrated low-power
sensors. Hence, IoT-based devices are particularly promising for applications in medicine and healthcare
[15]-[18]. Such devices have greater potential to reduce the ever-increasing cost of care in developed as well
as developing countries. To provide quality of care and remote healthcare monitoring facilities, it is
imperative to have an open-source hardware platform for modelling, analysis, and prototyping. In this paper,
results are reported from experiments conducted in both software and hardware. The hardware chip is
fabricated with a 0.35 um complementary metal-oxide-semiconducto (CMOS) process [19], [20]. This paper
offers a systematic approach to building a platform from software prototyping, silicon implementation,
electrical characterization, and empirical modelling. Section 2 details the research method where spiking
neural model was investigated in software domain. Section 3 presents the hardware circuits to capture neural
responses akin to the biological neurons. The hardware structures were fabricated and characterized whereas
the data extracted through the fabricated device was empirically modelled in the form of mathematical
equations and embedded into software domain for benchmarking applications. It is envisaged that this work
will offer an open-source platform where generic neural fabric is available to researchers and practitioners for
prototyping applications in the domain of neuromorphic hardware. Our long-term objective is to offer the
very first, an open-source neuromorphic hardware platform for artificial intelligence (AI) enabled healthcare
devices and related industrial applications.
2. RESEARCH METHOD
Recent advances in machine learning necessitate computation-efficient paradigms on both software
and hardware platforms. When spike-based computation models, such as spiking neural networks (SNNs),
are run on neuromorphic hardware, they have a huge potential to reduce energy usage. Nonetheless, due to
the large number of neurons and synapses required at a biological scale, simulating and mapping SNN
architectures on a hardware platform is a daunting task. The proposed method takes a novel approach by
simulating neuronal entities in software before capturing and replicating their dynamics. Rather than
designing and fabricating custom hardware circuits and architectures for a specific application, generic
customised neural cells are investigated and developed. Empirical models developed from the data extracted
through the fabricated structures. This approach offers true replication of the neuromorphic hardware which
is cost-effective and saves time and effort to develop neuro-inspired applications. As spiking neurons are
considered more biologically plausible, they use spikes as the communication mechanism between different
entities. Depending on the model, each spike can be defined as a binary event where the actual timestamp of
the spike carries information. Spiking neuron communication behaviour is shown in Figure 1 where In are the
input currents, s synapses and n the neuron membrane which connects with following synapses and outputs
are generated, as denoted by On.
2.1. Neuron circuit simulations
In this work, an SRM model is considered for investigation and implementation. The membrane
potential of the model can be represented by (1), whereas postsynaptic potential is represented by (2).
𝑉
𝑚(𝑡) = ∑ 𝜂𝑖(𝑡 − 𝑡𝑖
(𝑓)
) +
𝑡𝑖
(𝑓)
∈ 𝐹𝑖
∑ ∑ 𝑤𝑖𝑗𝜀𝑖𝑗 (𝑡−𝑡(𝑓)
)
𝑡𝑗
(𝑓)
𝜖𝐹𝑗
𝑗 𝜖 Γ𝑖
(1)

Int J Reconfigurable & Embedded Syst ISSN: 2089-4864 
OSPEN: an open source platform for emulating neuromorphic hardware (Arfan Ghani)
3
𝜀𝑖𝑗(𝑡 − 𝑡(𝑓)
) =
𝑡
𝜏
exp (−
𝑡
𝜏
) (2)
As shown in (1), the expressions represent as follows: Vm is the membrane potential, 𝜂𝑖 (action
potential), 𝑡𝑖
(𝑓)
(the last firing time of neuron I), 𝑡𝑗
(𝑓)
pre-synaptic input spike from neuron j), 𝜀𝑖𝑗 (𝑡−𝑡(𝑓)
) (the
PSP of neuron i caused by a spike from neuron j), 𝑤𝑖𝑗 (synaptic strength), 𝐹𝑖 (the set of all firing times of
neuron I, and Γi represents the set of all presynaptic neurons. Whereas in (2), tau represents the rise and fall
time of the postsynaptic response.
In order to replicate and capture the neural dynamics, the simulated neural circuit is shown in
Figure 2. As shown in the Figure 2, input stimuli (I1 and I2) are connected with two SRM neurons (N1 and
N2) respectively where N1 is considered excitatory and N2 inhibitory. The neurons are connected via static
synapses. The parameters used for simulating this circuit are shown in Table 1. To have insight into the
biological domain, different papers were surveyed to collect realistic biological data sets. We need to know
our limitations in terms of hardware implementation and the values required to develop a biologically
plausible SNN platform. These parameters have also been widely used in computing-related tasks. For the
neuron model and related parameters used for simulations, we refer the reader to [21] and [2].
Figure 1. Standard spiking neuron with synaptic connections
Figure 2. SRM circuit diagram
Table 1. Simulation parameters [9]
Parameters Units
Membrane time constant 30 ms
Absolute refractory period 3 ms
Reset voltage 13.5 mV
Resting voltage 0 mV
Threshold voltage 15 mV
Background noise 13.5 nA
Input resistance 1 MOhms
Synaptic delay 1 ms
The software simulation results are shown in Figure 3 where the top plot shows the input stimuli
(analogue and digital) and the second plot from the top shows the output spike times at different time steps.
Plot 3 from the top shows the membrane potential of both neurons and the bottom plot shows the
postsynaptic potentials for all four synapses. As shown in the figure, two inputs (analogue and spiking) were

 ISSN: 2089-4864
4
connected with two neurons (excitatory and inhibitory) via synapses. Whereas the main difference between
excitatory and inhibitory neurons is that the excitatory neurons fire an action potential in the postsynaptic
neuron and inhibitory neurons inhibit the firing of an action potential. For further details on synaptic
potentials and membrane dynamics, we refer the reader to [2].
Figure 3. SRM circuit response in terms of membrane voltages and synaptic potentials
2.2. Postsynaptic response and membrane potential
As information in spiking neurons is conveyed through spikes where postsynaptic response plays an
important role in neural dynamics. By increasing or decreasing the rise and fall times of the postsynaptic
potential, the output firing patterns change to a greater extent [3]. To further examine the impact of input
spike train on postsynaptic responses and membrane potentials, a rather simple model is emulated in
software. This model includes a threshold voltage which is compared with the membrane potential. An
output spike is generated if the membrane potential exceeds the threshold value, whereas a spike threshold is
calculated for each time step. Figure 4 elaborates an input spike train, postsynaptic responses, corresponding
membrane potentials, and output spike train. It is observed that during the rise and fall time of the
postsynaptic potentials, the dynamics of a neuron change. The impact of 𝜏 was observed on the neural
membrane accordingly.
The top plot in Figure 4 shows an input spike train whereas the middle plot shows the postsynaptic
response and the bottom plot shows the membrane potential and output spikes. It can be seen that each input
spike creates a postsynaptic response at a specific time step which is accumulated into the neuron’s
membrane. Once the membrane potential exceeds a certain threshold, output spikes are generated at a
specific time interval. It can be inferred from these simulations that the spike times play an important role in
neural signal processing where the output spikes translate the input spike frequency. The main purpose of
simulating these circuits was to capture some of the neural dynamics and translate them into hardware
structures for large-scale implementations.

5
Figure 4. This figure shows the input spike train, postsynaptic responses and corresponding membrane
potentials
2.3. Hardware modelling of postsynaptic potentials
Once the basic functionality of neural entities was examined through software simulations, hardware
fabric was designed, fabricated, and characterized. The chip was fabricated by Europractice with the 0.35 um
AMS n-well process. The basic circuits of synapses and neurons are shown in Figure 5. Further details are
provided in [19], [20], [22].
To benchmark the characteristics of neural cells, several synapse/neuron blocks were implemented
where each circuit on the chip comprised a synapse cell connected to a neuron circuit. As shown in Figure 5,
in the absence of synaptic activity, the VIN node sits at a voltage slightly lower than VDD, due to the leakage
current at that point and the need for this to be sourced by M1. When a pre-synaptic neuron fires (VPRES),
electronic charge is transferred onto the output node of the synapse, reducing the value of VIN. This transient
reduction in voltage is fed through a buffer and measured on an oscilloscope. The PSP is formed by the
charging and discharging of the capacitance, C present at the drain of M5. A current passing through M4, IM4
will charge the capacitor to a value VPSP, and it is subsequently discharged by the current flowing through
M5, IM5, which defines the fall time of the PSP. Ideally, IM4 >> IM5, so that the leakage current has
no discernable effect on the charging of the capacitor. Once VPSP reaches the switching threshold for the
inverter M6/M7, the neuron ‘fires’, producing a voltage pulse at the VOUT node, before VPSP is discharged
through M10. The layout of the chip is shown in Figure 6 which implements the circuits shown in Figure 5,
alongside structures/circuits for characterisation and testing. An XOR benchmark was implemented on the
chip and characterized. Further details on hardware implementation and benchmark applications are provided
in [19], [23].
2.4. Hardware measurements
VW, VPRES, VLEAK, and VDD were supplied to the IC input using a semiconductor parameter analyzer,
providing independent control over each voltage. The measurements were taken from the chip by direct
probing. The outputs from the probe station were recorded using a Tektronix oscilloscope. Whereas voltage
VW sets the level of synaptic charge and VLEAK controls the fall time of the PSP. Charge transfer through the
synapse is initiated by the application of a single pulse to the VPRES terminal of the synapse. The magnitude
of the pulse is set to VDD, the 10-90% rise/fall times are 5 ns and the 50-50% pulse width is 20 ns. To
analyze PSP response from the fabricated chip, extracted data is plotted and to further investigate the
computational capability of fabricated structures, an empirical model was developed based on the data
extracted from the chip. Figure 7 shows the PSPs which are sampled for 0<VW<3.3 V in 50 mV increments
and VLEAK is set to 0.24 V.

 ISSN: 2089-4864
6
To provide a generic neural fabric to benchmark neuro-inspired applications, an empirical model
was developed by Gaussian curve fitting. The data extracted from the fabricated chip was used to develop the
empirical model as a function of weight. This model replicates neural dynamics and postsynaptic potential
measured through a chip with a similar time scale (Figure 7 (a) and Figure 7(b)). The mathematical equation
is shown in (3), where the response model is the product of three parts.
𝑦 = [𝑦0 + 𝐴𝑒
−
𝑥.(𝑥−𝑥𝑐)2
2𝑤2
] . (1 − 𝑒−𝑏𝑥). 𝜀(𝑥 − 𝑡𝑠𝑝𝑖𝑘𝑒) (3)
These three parts are an amplitude version of the Gaussian peak function, a Box Lucas function, and
a step function. Whereas yo, A, b, w and xc are real constants and 𝜀 is a response kernel that exhibits the
typical time course of an excitatory postsynaptic potential in terms of response voltage approaching zero
from above. By using this approach, the responses of the spikes with different synaptic weights were
modelled mathematically. It is particularly important to have an empirical model that mimics the actual
behaviour of the fabricated device.
Figure 5. Synapse and neuron circuit [19], [20] Figure 6. Chip layout (3.7x3.7 mm) [20]
(a) (b)
Figure 7. This figure shows the comparison between actual data extracted from the chip
and the empirical model for (a) a single and (b) multiple synapses

7
3. RESULTS AND DISCUSSION
Previous research in neuromorphic engineering is mainly done through hardware implementations
or software modelling. In particular, hardware implementations were constrained and were capable of
characterizing the effects of a few variables. It was mainly due to the number of experiments needed to
capture neural dynamics being specific and limited. Software-based approaches, nonetheless, capture high as
well as low-level dynamics, without regard to any transistor or layout-level aspects, hence lacking accuracy
at the device level. In order to observe neural dynamics at the device level and explore real-life engineering
applications, it would be unrealistic not to consider the necessary process and layout-related information. For
rapid development and prototyping of neuromorphic applications, novel approaches are needed to implement
and analyze neuro-inspired paradigms. This paper demonstrated the viability of implementing neural
dynamics on software and hardware platforms whereas the data extracted through fabricated neural structures
were empirically modelled and provided as an open-source [24]. To investigate software based spiking
neuron models in Python, readers are referred to [25]. The repository provided in [24] currently contains the
code, empirically modelled postsynaptic potentials, neural membrane potentials, and mathematical equations
that can be embedded into the software domain. The authors have provided this repository as an open-source
platform for the neuromorphic research community to make use of the existing code, equations, and models
and hope it would be further complemented with bespoke datasets and empirical models.
4. CONCLUSION
To summarize, this paper offers a proof-of-concept demonstration of the spiking neural circuits
capturing some of the neural dynamics. It is shown by experimental results (software and hardware) that it is
viable to look beyond traditional paradigms and explore neuro-inspired cells as computational entities.
Empirical models were developed from the data extracted through the fabricated device and accuracy was
confirmed. This work demonstrates the feasibility of an open-source neural hardware platform where neural
fabric can be provided for rapid investigation and development. Authors have set up a GitHub repository
where mathematical equations and MATLAB code for empirically modelled postsynaptic potentials and
neural membranes are provided. Due to the analogue nature of neural cells and synapses, millions of such
computational entities are possible to realize on-chip and with the provision of an open-source neuromorphic
repository, further applications related to machine learning, deep neural networks and IoTs can be explored.
ACKNOWLEDGEMENTS
The authors would like to thank Prof. Stephen Hall from the Faculty of Science and Engineering,
Liverpool University, UK for his feedback and valuable suggestions on the manuscript.
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BIOGRAPHIES OF AUTHORS
Arfan Ghani is working as an Associate Professor and has gained academic
qualifications and experience working in Denmark and the UK institutions including Ulster,
Coventry, and Newcastle. His industrial research and development experience includes
working at Intel Research, University of Cambridge and Vitesse Semiconductors Denmark.
Arfan is a member of IET, a Chartered Engineer (CEng) and a Fellow of the Higher Education
Academy in the UK. His research expertise includes developing technologies and direct
research in the multi-disciplinary area of neuromorphic computing, reconfigurable hardware
accelerators (FPGAs), smart IoT devices, and applied machine learning. He can be contacted at
email: Arfan.ghani@aurak.ac.ae.
Dr. Thomas Dowrick is a Senior Research Fellow at UCL’s WEISS Centre,
working on synthetic data for medical imaging applications, and software development for
Image Guided Surgery. He has extensive research experience in neuromorphic hardware,
medical imaging, and deployment of novel hardware/software for clinical investigations. He is
currently supported by an EPSRC Research Software Engineering Fellowship
(EP/V052438/1). He can be contacted at email: t.dowrick@ucl.ac.uk.
Prof. Liam J. McDaid received the B.Eng. degree (Hons.) in electrical and
electronics engineering and the PhD degree in solid-state devices from the University of
Liverpool, Liverpool, U.K., in 1985 and 1989, respectively. He is currently a Professor of
computational neuroscience at Ulster University, Derry, U.K., where he leads the
Computational Neuroscience and Neural Engineering Research Team. He is also involved in
the development of software/hardware models of neural-based computational systems with a
particular emphasis on the mechanisms that underpin self-repair in the human brain. He has co-
authored over 120 publications in his career to date. His current research interests include
modeling the role of glial cells in the functional and dysfunctional brain. Dr. McDaid received
several research grants in this domain. He is currently a collaborator on the Human Frontier
Science Program and Engineering and Physical Sciences Research Council-funded projects.
He can be contacted at email: lj.mcdaid@ulster.ac.uk.

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