An ultra-compact leaky integrate-and-fire neuron with long and tunable time constant utilizing pseudo resistors for spiking neural networks

Spiking neural networks (SNNs) inspired by biological neurons enable a more realistic mimicry of the human brain. To realize SNNs similar to large-scale biological networks, neuron circuits with high area efficiency are essential. In this paper, we propose a compact leaky integrate-and-fire (LIF) neuron circuit with a long and tunable time constant, which consists of a capacitor and two pseudo resistors (PRs). The prototype chip was fabricated with TSMC 65 nm CMOS technology, and it occupies a die area of 1392 m2. The fabricated LIF neuron has a power consumption of 6 W and a leak time constant of up to 1.2 ms (the resistance of PR is up to 600 MΩ). In addition, the time constants are tunable by changing the bias voltage of PRs. Overall, this proposed neuron circuit facilitates the very-large-scale integration (VLSI) of adaptive SNNs, which is crucial for the implementation of bio-scale brain-inspired computing.

Compact and Energy Efficient Neuron With Tunable Spiking Frequency in 22-nm FDSOI

In this paper, we present a mixed-signal integrate and fire neuron designed in a 22-nm FDSOI technology. In this novel design, we deploy the back-gate terminal of FDSOI technology for a tunable design. For the first time, we show analytically and with pre- and post-layout simulations a neuron with tunable spiking frequency using the back-gate voltage of FDSOI technology. The neuron circuit is designed in the sub-threshold region and dissipates an ultra-low energy per spike of the order of Femto Joules per spike. With the layout area of only 30um^2, this is the smallest neuron circuit reported to date.

Compact and Energy Efficient Neuron With Tunable Spiking Frequency in 22-nm FDSOI

In this paper, we present a mixed-signal integrate and fire neuron designed in a 22-nm FDSOI technology. In this novel design, we deploy the back-gate terminal of FDSOI technology for a tunable design. For the first time, we show analytically and with pre- and post-layout simulations a neuron with tunable spiking frequency using the back-gate voltage of FDSOI technology. The neuron circuit is designed in the sub-threshold region and dissipates an ultra-low energy per spike of the order of Femto Joules per spike. With the layout area of only 30um^2, this is the smallest neuron circuit reported to date.

Compact and Energy Efficient Neuron With Tunable Spiking Frequency in 22-nm FDSOI

<div>In this paper, we present an integrated and fire neuron designed in a 22-nm FDSOI technology. In this novel design, we deploy the back-gate terminal of FDSOI technology for a tunable design. For the first time, we show analytically and with pre- and post-layout simulations a neuron with tunable spiking frequency using the back-gate voltage of FDSOI technology. The neuron circuit is designed in the sub- hreshold region and dissipates an ultra-low energy per spike of the order of Femto Joules per spike. With the layout area of only 30 um2, this is the smallest neuron circuit reported to date.</div>

Compact and Energy Efficient Neuron With Tunable Spiking Frequency in 22-nm FDSOI

<div>In this paper, we present an integrated and fire neuron designed in a 22-nm FDSOI technology. In this novel design, we deploy the back-gate terminal of FDSOI technology for a tunable design. For the first time, we show analytically and with pre- and post-layout simulations a neuron with tunable spiking frequency using the back-gate voltage of FDSOI technology. The neuron circuit is designed in the sub- hreshold region and dissipates an ultra-low energy per spike of the order of Femto Joules per spike. With the layout area of only 30 um2, this is the smallest neuron circuit reported to date.</div>

Memristor-CMOS Hybrid Neuron Circuit with Nonideal-Effect Correction Related to Parasitic Resistance for Binary-Memristor-Crossbar Neural Networks

Voltages and currents in a memristor crossbar can be significantly affected due to nonideal effects such as parasitic source, line, and neuron resistance. These nonideal effects related to the parasitic resistance can cause the degradation of the neural network’s performance realized with the nonideal memristor crossbar. To avoid performance degradation due to the parasitic-resistance-related nonideal effects, adaptive training methods were proposed previously. However, the complicated training algorithm could add a heavy computational burden to the neural network hardware. Especially, the hardware and algorithmic burden can be more serious for edge intelligence applications such as Internet of Things (IoT) sensors. In this paper, a memristor-CMOS hybrid neuron circuit is proposed for compensating the parasitic-resistance-related nonideal effects during not the training phase but the inference one, where the complicated adaptive training is not needed. Moreover, unlike the previous linear correction method performed by the external hardware, the proposed correction circuit can be included in the memristor crossbar to minimize the power and hardware overheads for compensating the nonideal effects. The proposed correction circuit has been verified to be able to restore the degradation of source and output voltages in the nonideal crossbar. For the source voltage, the average percentage error of the uncompensated crossbar is as large as 36.7%. If the correction circuit is used, the percentage error in the source voltage can be reduced from 36.7% to 7.5%. For the output voltage, the average percentage error of the uncompensated crossbar is as large as 65.2%. The correction circuit can improve the percentage error in the output voltage from 65.2% to 8.6%. Almost the percentage error can be reduced to ~1/7 if the correction circuit is used. The nonideal memristor crossbar with the correction circuit has been tested for MNIST and CIFAR-10 datasets in this paper. For MNIST, the uncompensated and compensated crossbars indicate the recognition rate of 90.4% and 95.1%, respectively, compared to 95.5% of the ideal crossbar. For CIFAR-10, the nonideal crossbars without and with the nonideal-effect correction show the rate of 85.3% and 88.1%, respectively, compared to the ideal crossbar achieving the rate as large as 88.9%.

Integrate-and-Fire Neuron Circuit Without External Bias Voltages

In this study, we propose an integrate-and-fire (I&amp;F) neuron circuit using a p-n-p-n diode that utilizes a latch-up phenomenon and investigate the I&amp;F operation without external bias voltages using mixed-mode technology computer-aided design (TCAD) simulations. The neuron circuit composed of one p-n-p-n diode, three MOSFETs, and a capacitor operates with no external bias lines, and its I&amp;F operation has an energy consumption of 0.59 fJ with an energy efficiency of 96.3% per spike. The presented neuron circuit is superior in terms of structural simplicity, number of external bias lines, and energy efficiency in comparison with that constructed with only MOSFETs. Moreover, the neuron circuit exhibits the features of controlling the firing frequency through the amplitude and time width of the synaptic pulse despite of the reduced number of the components and no external bias lines.