Hi greenwaves,
This article is from the journal you found.
Although the author makes reference to True North and Liohi, she assumes that all SNNs are analog.
https://iopscience.iop.org/article/10.1088/2634-4386/ac4918
Ferroelectric-based synapses and neurons for neuromorphic computing
Erika Covi3,1, Halid Mulaosmanovic1, Benjamin Max2, Stefan Slesazeck3,1 and Thomas Mikolajick1,2
Published 7 February 2022 • © 2022 The Author(s). Published by IOP Publishing Ltd
Neuromorphic Computing and Engineering ,
Volume 2,
Number 1Focus Issue on Hafnium Oxide-Based Neuromorphic DevicesCitation Erika Covi
et al 2022
Neuromorph. Comput. Eng. 2 012002
A possible solution to optimize energy efficiency is to enable computing right next to where the data is collected, e.g., the sensor [4, 5]. In this respect, one of the most promising approaches is the neuromorphic approach, in particular the brain-inspired spiking neural network (SNN) [6]. SNNs owe their power efficiency to their hardware architecture, which uses artificial neurons and synapses to overcome the physical separation between memory and central processing unit typical of standard von Neumann architectures, and to the adoption of an asynchronous event-based approach that elaborates the information, in the form of spikes, only when available. Both industry and academia have already demonstrated interest in SNNs, defined as the third generation of neural networks, which resulted in remarkable neuromorphic processors such as IBM's TrueNorth [7], SpiNNaker [8], Intel's Loihi [9], DYNAP-SE [10], ODIN [11], and MorphIC [12]. However, in pure complementary metal-oxide-semiconductor (CMOS) solutions, the basic building elements of an SNN, i.e., neurons and synapses, present some non-negligible disadvantages. Indeed, neurons are rather complicated to realize. The simplest versions reproducing integrate-and-fire (I&F) behavior need at least an integrator, which also implies using a capacitor, which can occupy a rather large area depending on the time constants that need to be used, as well as a comparator. More complicated versions may need current sensors, capacitors, analogue-to-digital (ADC), and digital-to-analogue converters, with consequent area and power consumption requirements [
13].
...
Memristive devices show a broad range of excellent properties, including non-volatile memory, analogue behaviour, high scalability, high read/program speed, high energy efficiency, and programming voltages comparable with the power supply of typical neuromorphic chips [
14,
15]
Once again we see academia's blinkered concentration on analog SNNs. Analog behaviour also has the baggage of component variations due to manufacturing tolerance limits.
I've looked high and low, but can't find a capacitor in Akida's integrate and fire circuit. ['pologies if you've seen this before. Can't help myself - love this circuit - a thing of beauty]:
View attachment 2412