Computing Resistance-Style Image Sensors for Artificial Neural Networks

Zhao, Guihua; Peng, Yating; Wang, Yizhan; Wan, Caihua; Liu, Xianping; Han, Xiufeng; Chen, Weichong; Yu, Zhiyi

doi:10.1109/jiot.2022.3219847

Cited by 2 publications

(4 citation statements)

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“…The spintronic devices are regarded as the potential candidate to implement in-sensor computing since its unique feature of multi-physics field sensing and nonlinear characters. [5,23,24] The STT-MTJ devices could keep in steady oscillation is referred as STT oscillators (STOs). The oscillation frequency and output amplitude of the STO device can be regulated by both DC bias current and external DC magnetic field.…”

Section: The Modified Bp Learning Algorithmmentioning

confidence: 99%

“…Research has attempted to implement the in‐sensor computing synapses for the artificial neural network (ANN) using various new types of devices, such as optoelectronic, [ 8,9 ] ferroelectric, [ 10 ] memristor, [ 11 ] 2D material devices, [ 12 ] as well as spintronic devices. [ 5,13 ] In these works, most of the existing in‐sensor computing synapses proposed mainly perform multiplication, [ 4,14 ] while the nonlinearity of hardware synapses is viewed as defective. The principal reason is that the nonlinearity of synapse degrades the accuracy under the traditional network and training algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the traditional architecture cannot meet the time and power requirements in the big data era. The in-sensor computing as an emerging direction, [2][3][4][5][6] implementing signal process in sensors, has been regarded as one of the most potential architectures to break the acquisition wall, since it can significantly reduce the redundant data processing and movement fundamentally, so as to improve the sampling efficiency, and reduce the delay and energy consumption. [7] The core foundation of in-sensor computing is the construction of the neural network using artificial synapses and neurons.…”

Section: Introductionmentioning

confidence: 99%

“…Research has attempted to implement the in-sensor computing synapses for the artificial neural network (ANN) using various new types of devices, such as optoelectronic, [8,9] ferroelectric, [10] memristor, [11] 2D material devices, [12] as well as spintronic devices. [5,13] In these works, most of the existing in-sensor…”

Section: Introductionmentioning

confidence: 99%

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Spin‐Transfer‐Torque Magnetic Tunnel Junction Nonlinear In‐Sensor Computing Synapse for Improving the Performance of the Feedforward Neural Network

Ji,

Wang,

Yang

et al. 2024

Advanced Intelligent Systems

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In‐sensor computing architecture has a great advantage especially in massive data sampling, transfer, and processing compared with the separated intelligent sensor systems. However, most of the in‐sensor computing device is proposed based on the traditional neural network model, where the synapse performs linear multiplication of input and weight. This approach fails to make the most use of the nonlinearity of in‐sensor computing devices. Therefore, in this article, first a modified feedforward neural network model with the nonlinear in‐sensor computing synapse (NSCS) located at the input layer is presented, and the backpropagation (BP) algorithm is modified to train the network. Then, the nonlinear characteristics of the NSCS composed of the spin‐transfer‐torque magnetic tunnel junction (STT–MTJ) devices and simple complementary metal‐oxide‐semiconductor (CMOS) circuit are analyzed. Based on the nonlinear response of STT–MTJ NSCS, the small‐scale network with NSCS synapse is experimented on the Modified National Institute of Standards and Technology dataset and compared with the traditional network of the same network size. In the simulation result, it is shown that better performance can be achieved with the STT–MTJ NSCS, including a 2–15 times improvement in convergence speed and a 2.5%–5.1% increase in accuracy.

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Section: The Modified Bp Learning Algorithmmentioning

confidence: 99%