The spiking neural network (SNN) is regarded as the third generation of an artificial neural network (ANN). In order to realize a high-performance SNN, an integrate-and-fire (I&F) neuron, one of the key elements in an SNN, must retain the overflow in its membrane after firing. This paper presents an analog CMOS I&F neuron circuit for overflow retaining. Compared with the conventional I&F neuron circuit, the basic operation of the proposed circuit is confirmed in a circuit-level simulation. Furthermore, a single-layer SNN simulation was also performed to demonstrate the effect of the proposed circuit on neural network applications by comparing the raster plots from the circuit-level simulation with those from a high-level simulation. These results demonstrate the potential of the I&F neuron circuit with overflow retaining characteristics to be utilized in upcoming high-performance hardware SNN systems.

Spiking neural networks (SNNs) are considered as the third generation of artificial neural networks, having the potential to improve the energy efficiency of conventional computing systems. Although the firing rate of a spiking neuron is an approximation of rectified linear unit (ReLU) activation in an analogvalued neural network (ANN), there remain many challenges to be overcome owing to differences in operation between ANNs and SNNs. Unlike actual biological and biophysical processes, various hardware implementations of neurons and Snns do not allow the membrane potential to fall below the resting potential-in other words, neurons must allow the sub-resting membrane potential. Because there occur an excitatory post-synaptic potential (EPSP) as well as an inhibitory post-synaptic potential (ipSp), negatively valued synaptic weights in Snns induce the sub-resting membrane potential at some time point. If a membrane is not allowed to hold the sub-resting potential, errors will accumulate over time, resulting in inaccurate inference operations. This phenomenon is not observed in ANNs given their use of only spatial synaptic integration, but it can cause serious performance degradation in SNNs. In this paper, we demonstrate the impact of the sub-resting membrane potential on accurate inference operations in SNNs. Moreover, several important considerations for a hardware SNN that can maintain the sub-resting membrane potential are discussed. All of the results in this paper indicate that it is essential for neurons to allow the sub-resting membrane potential in order to realize high-performance SNNs.

Spiking neural networks (SNNs) have attracted many researchers’ interests due to its biological plausibility and event-driven characteristic. In particular, recently, many studies on high-performance SNNs comparable to the conventional analog-valued neural networks (ANNs) have been reported by converting weights trained from ANNs into SNNs. However, unlike ANNs, SNNs have an inherent latency that is required to reach the best performance because of differences in operations of neuron. In SNNs, not only spatial integration but also temporal integration exists, and the information is encoded by spike trains rather than values in ANNs. Therefore, it takes time to achieve a steady-state of the performance in SNNs. The latency is worse in deep networks and required to be reduced for the practical applications. In this work, we propose a pre-charged membrane potential (PCMP) for the latency reduction in SNN. A variety of neural network applications (e.g., classification, autoencoder using MNIST and CIFAR-10 datasets) are trained and converted to SNNs to demonstrate the effect of the proposed approach. The latency of SNNs is successfully reduced without accuracy loss. In addition, we propose a delayed evaluation method (DE), by which the errors during the initial transient are discarded. The error spikes occurring in the initial transient is removed by DE, resulting in the further latency reduction. DE can be used in combination with PCMP for further latency reduction. Finally, we also show the advantages of the proposed methods in improving the number of spikes required to reach a steady-state of the performance in SNNs for energy-efficient computing.

No abstract

A multi-nanosheet field-effect transistor (mNS-FET) device was developed to maximize gate controllability while making the channel in the form of a sheet. The mNS-FET has superior gate controllability for the stacked channels; consequently, it can significantly reduce the short-channel effect (SCE); however, punch-through inevitably occurs in the bottom channel portion that is not surrounded by gates, resulting in a large leakage current. Moreover, as the size of the semiconductor device decreases to several nanometers, the influence of the parasitic resistance and parasitic capacitance increases. Therefore, it is essential to apply design–technology co-optimization, which analyzes not only the characteristics from the perspective of the device but also the performance from the circuit perspective. In this study, we used Technology Computer Aided Design (TCAD) simulation to analyze the characteristics of the device and directly fabricated a model that describes the current–voltage and gate capacitance characteristics of the device by using Berkeley short-channel insulated-gate field-effect transistor–common multi-gate (BSIM–CMG) parameters. Through this model, we completed the Simulation Program with Integrated Circuit Emphasis (SPICE) simulation for circuit analysis and analyzed it from the viewpoint of devices and circuits. When comparing the characteristics according to the presence or absence of bottom oxide by conducting the above research method, it was confirmed that subthreshold slope (SS) and drain-induced barrier lowering (DIBL) are improved, and power and performance in circuit characteristics are increased.

No abstract

In this study on multi-nanosheet field-effect transistor (mNS-FET)—one of the gate-all-around FETs (GAAFET) in the 3 nm technology node dimension—3D TCAD (technology computer-aided design) was used to attain optimally reduced substrate leakage from options including a punch-through-stopper (PTS) doping scheme and a bottom oxide (BO) scheme for bottom isolation, with the performance improvement being shown in the circuit-level dynamic operation using the mNS-FET. The PTS doping concentration requires a high value of >5 × 1018 cm−3 to reduce gate induced drain leakage (GIDL), regardless of the presence or absence of the bottom isolation layer. When the bottom isolation is applied together with the PTS doping scheme, the capacitance reduction is larger than the on-state current reduction, as compared to when only the PTS doping concentration is applied. The effects of such transistor characteristics on the performance and capabilities of various circuit types—such as an inverter ring oscillator (RO), a full adder (FA) circuit, and a static random-access memory (SRAM)—were assessed. For the RO, applying BO along with the PTS doping allows the operating speed to be increased by 11.3% at the same power, or alternatively enables 26.4% less power consumption at the same speed. For the FA, power can be reduced by 6.45%, energy delay product (EDP) by 21.4%, and delay by 16.8% at the same standby power when BO and PTS are both applied. Finally, for the SRAM, read current (IREAD) increased by 18.7% and bit-line write margin (BWRM) increased by 12.5% at the same standby power. Through the circuit simulations, the Case 5 model (PTS doping concentration: 5.1 × 1018 cm−3, with BO) is the optimum condition for the best device and circuit performance. These observations confirm that PTS and bottom isolation applications in mNS-FETs can be utilized to enable the superior characteristics of such transistors to translate into high performance integrated circuits.

NOR/AND flash memory was studied in neuromorphic systems to perform vector-by-matrix multiplication (VMM) by summing the current. Because the size of NOR/AND cells exceeds those of other memristor synaptic devices, we proposed a 3D AND-type stacked array to reduce the cell size. Through a tilted implantation method, the conformal sources and drains of each cell could be formed, with confirmation by a technology computer aided design (TCAD) simulation. In addition, the cell-to-cell variation due to the etch slope could be eliminated by controlling the deposition thickness of the cells. The suggested array can be beneficial in simple program/inhibit schemes given its use of Fowler–Nordheim (FN) tunneling because the drain lines and source lines are parallel. Therefore, the conductance of each synaptic device can be updated at low power level.