Pulse neuron circuit based on tunneling field effect transistor

A tunneling field effect and neuron technology, applied in the direction of pulse technology, logic circuit, biological neural network model, etc., can solve the problems of low hardware overhead, achieve low hardware overhead, reduce short-circuit energy consumption, and save hardware overhead

Active Publication Date: 2018-11-23
PEKING UNIV
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  • Application Information

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Problems solved by technology

[0004] Aiming at the problems existing in the above prior art, the present invention proposes a pulse neuron circuit based on tunneling field effect transistor; compared with the implementation based on traditional MOSFET, the pulse neuron circuit based on tunneling field effect transistor of the present invention The meta-circuit has lower hardware overhead, and can effectively reduce the short-circuit

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  • Pulse neuron circuit based on tunneling field effect transistor

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Embodiment Construction

[0013] Below in conjunction with the accompanying drawings, the present invention will be further described through specific embodiments.

[0014] like figure 1 As shown, this embodiment implements a pulse neuron circuit based on a tunneling field effect transistor under a silicon-based CMOS compatible process, including a capacitor C mem , leak and reset tube M 1 , positive feedback tube M 2 , two-stage series inverter M 3 -M 6 ; Among them, the capacitance C mem It is used to simulate the cell membrane capacitance of biological neurons, accumulating the charge brought by the input post-synaptic current (PSC), and its two ends are respectively connected to the input terminal V of the first-stage inverter mem with a fixed resting voltage V rest , this fixed resting voltage V rest Slightly higher than GND but less than the logic threshold level of the first stage inverter; leakage and reset transistor M 1 It is an N-type SiTFET device that provides a leakage and reset p...

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Abstract

The invention provides a pulse neuron circuit based on a tunneling field effect transistor, and belongs to the technical field of pulse neuron in neuromorphic calculation. The circuit comprises a capacitor, a leakage and reset tube, a positive feedback tube, and two stages of inverters connected in series, wherein the capacitor is used for simulating the cytomembrane capacitance of a biological neuron and accumulating charges caused by the input post-synaptic current; the leakage and reset tube is an N type TFET device that provides a leakage and reset path for the charges accumulated on the capacitor; the positive feedback tube is a P type TFET device that supplements charges for the capacitor when the input of a first stage of inverter approaches to a logic threshold level thereof; and the two stages of inverters connected in series are composed of complementary TFETs for amplifying the voltage change at an input end. Compared with the implementation mode based on the traditional MOSFET, the pulse neuron circuit provided by the invention has lower hardware overhead, and can effectively reduce the short circuit energy consumption every time when the circuit releases the pulse, sothat the circuit has higher energy efficiency.

Description

technical field [0001] The present invention relates to the physical realization of spiking neurons in neuromorphic computing, in particular to a spiking neuron circuit based on tunneling field effect transistors. Background technique [0002] Today's society is in the era of data explosion and artificial intelligence, and data processing and computing face enormous challenges. The traditional Von Neumann computing architecture is limited by the slowdown of Moore's Law and the bottleneck of its own memory wall. The improvement of computing power tends to be saturated and the degree of intelligence is low. It is increasingly unable to meet the needs of massive data processing and intelligent tasks, and other solutions are required. Program. Inspired by the brain, the neural network computing architecture came into being. In recent years, artificial neural networks represented by deep neural networks have achieved great success in the field of perceptual intelligence such as...

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Application Information

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IPC IPC(8): H03K19/094G06N3/06G06N3/063
CPCG06N3/061G06N3/063H03K19/094
Inventor 黄如陈诚黄芊芊
Owner PEKING UNIV
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