One Transistor One Electrolyte‐Gated Transistor Based Spiking Neural Network for Power‐Efficient Neuromorphic Computing System

Abstract

Neuromorphic computing powered by spiking neural networks (SNN) provides a powerful and efficient information processing paradigm. To harvest the advantage of SNNs, compact and low‐power synapses that can reliably practice local learning rules are required, posing significant challenges to the conventional silicon‐based platform in terms of area‐ and energy‐efficiency, as well as computing throughput. Here, electrolyte‐gated transistors (EGTs) paired with transistors are employed to implement power‐efficient neuromorphic computing systems. The one‐transistor‐one‐EGT (1T1E) synapse not only alleviates the self‐discharging of EGT but also provides a flexible and efficient way to practice the important spike‐timing‐dependent plasticity learning rule. Based on that, an SNN with a temporal coding scheme is implemented for associative memory that can learn and recover images of handwritten digits with high robustness. Thanks to the temporal coding scheme and low operation current of EGTs, the energy‐efficiency of 1T1E‐based SNN is ≈30× lower than that of the prevalent rate coding scheme, and the peak performance is estimated to be 2 pJ/SOP (picojoule per synaptic operation) at the training phase and 80 TOPs−1 W−1 (tera operations per second per watt) at inference phase, respectively. These results pave the way for power‐efficient neuromorphic computing systems with wide applications for edge computing.

Emerging Optical In‐Memory Computing Sensor Synapses Based on Low‐Dimensional Nanomaterials for Neuromorphic Networks

“…With the complex visual neuron network system, the external information is transmitted to the visual cortex of the human brain and then explained with various characteristics such as wavelength, color, intensity, and frequency. [8,14,52,[129][130][131][132] Different external stimuli resulted in Reproduced with permission. [131] Copyright 2018, Nature Publishing Group.…”

“…With the complex visual neuron network system, the external information is transmitted to the visual cortex of the human brain and then explained with various characteristics such as wavelength, color, intensity, and frequency. [ 8,14,52,129–132 ] Different external stimuli resulted in cognition and memorizing variation, which is associated with the neural function in the biological system. [ 51,133–138 ] The application research on optical IMCS synapses is always inseparable from the emulation of artificial neural performance such as memorizing and forgetting, which is related to the synaptic short‐term memory (STM) and long‐term memory (LTM).…”

“…With the repeated and frequent stimuli (rehearsal), the STM can be transmitted to LTM before the occurrence of the forgetting behavior. [ 10,11,106,107,112,129,140 ] The transition process can be compared to the learning behavior, as illustrated in Figure a. In contrast, compared with artificial synaptic devices, biological synapses demonstrate the sensory memory (SM) before the STM, which is associated with the stimuli perception of the human sensory organs.…”

Realization of Enhanced Long‐Term Visual Memory for Indium–Gallium–Zinc Oxide‐Based Optical Synaptic Transistor

“…[18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33] Different types of devices, such as memristors, field-effect transistors, and phase change memories, have been studied for the application of artificial synapses. [16,18,19,[24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41]44] Additionally, numerous materials including but not limited to low-dimensional materials, perovskites, oxide semiconductors, and organic materials have been applied to artificial synapses. [16,18,19,[24][25][26][27][28][29][30][31]…”

Ion-dynamic Capacitance Enables Multimode Transistors and Multimode Neural Networks

“…[51][52][53] While the ANN and RNN use current levels, the SNN uses sparse current spikes with high information density and consumes longer time but much lower energy cost. 54,55 Compared with other electronic devices, such as FeFET, phase change memory, resistive memory, etc., the realization of various neural networks shows the potential of multimode transistor in neuromorphic computing (see Supplementary Fig. 28).…”