![]() However, due to the limitations and deficiencies of array fabrication technology for three-terminal neural devices, synaptic transistors as cross-bar weights combined with functional circuits are rarely explored to completely simulate the neural network 7. From the perspective of energy consumption, three-terminal neural devices have more potential to approach the power of the human brain (25 W) in large-scale computing 6. Additionally, the NeuRRAM-a chip allows for flexible reconfiguration of CIM cores to accommodate diverse model architectures 4, 5. It also boasts energy efficiency that is twice as good as previous state-of-the-art RRAM-CIM chips across different computational bit-precisions. ![]() The NeuRRAM-a chip is an advanced RRAM-based CIM chip that offers comparable inference accuracy to software models with four-bit weights for various AI tasks. In recent years, resistive random access memories (RRAMs) as memristors have been integrated with microprocessors and peripheral circuits to realize the artificial intelligence (AI) functionalities of neural networks 2, 3. Computing in memory (CIM) has the same protocols and standards for storage and memory, which is the top research for neuromorphic computing 1. Similar content being viewed by othersĬompared with traditional chips, nonvolatile neural devices have competitive advantages which includes low energy consumption and high-speed parallel operation. This work represents an advanced contribution to brain-like chips and promotes the systematization and diversification of artificial intelligence. SNNs deeply fused by synaptic transistors are designed to recognize the 40 different frequencies of EEG and improve accuracy to 95.1%. Finally, we recode the steady-state visual evoked potentials (SSVEPs) belonging to the electroencephalogram (EEG) with filter characteristics of LIF. The leaky integrator block, firing/detector block and frequency adaptation block instantaneously release the accumulated voltage to form pulses. In addition, the postsynaptic currents of the channel directly connect to the very large scale integration (VLSI) implementation of the LIF mode that can convert high-frequency information into spare pulses based on the threshold of membrane potential. Furthermore, the update rule of iteration weight in the backpropagation based on the time interval between presynaptic and postsynaptic pulses is extracted and fitted from the STDP. Significantly, three kinds of typical functions between neurons, the memory function achieved through the hippocampus, synaptic weight regulation and membrane potential triggered by ion migration, are effectively described through short-term memory/long-term memory (STM/LTM), long-term depression/long-term potentiation (LTD/LTP) and LIF, respectively. Here, a neural device is first demonstrated by zeolitic imidazolate frameworks (ZIFs) as an essential part of the synaptic transistor to simulate SNNs. The leaky integration and firing (LIF) model and spike-timing-dependent plasticity (STDP) are the fundamental components of SNNs. This work provides a feasible strategy to construct neuromorphic computing device with ultra-low energy consumption.Spiking neural networks (SNNs) have immense potential due to their utilization of synaptic plasticity and ability to take advantage of temporal correlation and low power consumption. Compared with the traditional floating-gate transistors, the designed device exhibits the distinct characteristics of visual synapse behaviors without the electrostatic aid gate, including the multilevel storage property of 13 stages, paired-pulse facilitation, the transition of short time plasticity to long time plasticity, and learning-forgetting-learning. Here, we propose a structure of the electrostatic aid-free photo-floating gate transistor based on the MoS 2/MoO x/WSe 2 heterojunctions, in which the MoO x acts as a unipolarity barrier layer and WSe 2 functions as a photo-floating gate layer. However, most of synaptic behaviors normally need the aid of electrostatic gate voltage, which induces considerable consumption. Synaptic transistors are important component of neuromorphic computing systems, which is promising to reduce data traffic, time delay, and energy cost.
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