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Composite acquisition and processing system based on photoelectric neural network

A neural network, acquisition and processing technology, applied in biological neural network models, neural learning methods, etc., can solve the increased time and energy consumption of sensor array serial data transmission, cannot meet the rapid growth of data processing, and data transmission cannot Completely parallel and other problems, to achieve the effect of being conducive to separating excitons, minimizing hardware consumption, and high photoelectric conversion efficiency

Pending Publication Date: 2020-03-06
FUDAN UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Second, peripheral circuits, such as amplifiers, addressing networks, and analog-to-digital converters (ADCs), need to match the difference between sensor output and network input. These complex modules not only consume more energy but also occupy a large area of ​​the device, so to a great extent increased cost of
At the same time, since the data transmission cannot be fully parallelized, the serial data transmission from the sensor array to the processing module significantly increases the time and energy consumption, which becomes a huge obstacle that limits further development in traditional systems.
All limitations stem from the separation of sensing and processing modules, which obviously cannot meet the rapidly growing demands of data processing

Method used

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  • Composite acquisition and processing system based on photoelectric neural network
  • Composite acquisition and processing system based on photoelectric neural network
  • Composite acquisition and processing system based on photoelectric neural network

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Experimental program
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Effect test

Embodiment 1

[0042] An efficient and low-cost photonic neural network (PNN) structure such as figure 1 Shown: From left to right are input layer, hidden layer and output layer. The system is a two-layer neuromorphic network with 784, 30 and 10 nodes in each layer. 784 × 30 photoelectric synapses were arranged between the input layer and the hidden layer, and 30 × 10 normal synapses were arranged between the hidden layer and the output layer.

[0043] The specific implementation method of embodiment 1 is as follows:

[0044] Two identical devices are grouped into one opto-synapse, that is, 30 opto-synapses sharing the same input neuron are grouped to receive the same light signal from an image pixel; 784 opto-synapses connected to the same hidden node share the same optical signal from the network of the same BP signal. The input signal is set to binary. A certain amount of synaptic weight change occurs only when the device is in light conditions and the BP signal is in the 'on' mode; ...

Embodiment 2

[0046] An efficient and low-cost photonic neural network (PNN) structure such as figure 1 Shown: From left to right are input layer, hidden layer and output layer. The system is a two-layer neuromorphic network with 784, 30 and 10 nodes in each layer. 784 × 30 photoelectric synapses were arranged between the input layer and the hidden layer, and 30 × 10 normal synapses were arranged between the hidden layer and the output layer. Develop an improved Widrow-Hoff algorithm.

[0047] The concrete algorithm of embodiment 2 is as follows:

[0048]

[0049] in, wxya ki is the weight change in the input layer, V is the input signal (only 1 and 0 here), is the BP signal; where, fg (n) is the target output, f (n) is the actual output, h ( i ) is the hidden layer output, W ij is the synaptic weight in the output layer. The threshold was set at 0.004G (G is the device conductivity).

[0050] The specific algorithm flow is as follows:

[0051] The first step is to train...

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Abstract

The invention belongs to the field of brain heuristic computation, and particularly relates to a composite acquisition and processing system based on a photoelectric neural network. According to the composite acquisition and processing system, a sensing function and a processing network are combined by adopting a photoelectric neural network; a dual-mode photoelectric synapse is adopted to realizea perceptual memory function; an improved BP algorithm neural network is adopted to enable photoelectric synaptic weight updating to depend on an input signal and a binary BP signal so as to reduce errors; the dual-mode photoelectric synapse adopts a bottom gate bottom contact field effect transistor, and a polyimide film is used as a flexible substrate of the device, and poly (9, 9-dioctylfluorene-co-dithio-benzene) is used as a light absorption material, and a single-walled carbon nanotube is used as a conductive channel to form a photoelectric material compound; and the composite acquisition and processing system is high in photoelectric conversion efficiency and small in equipment area, and the cost can be greatly reduced, and parallel transmission of data can be carried out, and timeand energy losses are reduced, and the system has important practical application significance.

Description

technical field [0001] The invention belongs to the technical field of brain-inspired computing, and in particular relates to a composite collection and processing system. Background technique [0002] Brain-inspired computing is considered to be a very promising solution to break through the bottleneck of traditional computing paradigms. Neuromorphic systems have achieved outstanding achievements at the software level, but this new field requires more innovations at the hardware level. So far, neuromorphic systems have been widely used, and researchers have studied the models and algorithms in detail. In general, parallelism, low energy consumption, and high fault tolerance are considered to be the main advantages of neural networks. But the network has yet to reach its full potential due to limitations in existing system architectures. Taking the image recognition system as an example, the image acquisition module is indispensable in the traditional system, and this mod...

Claims

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Application Information

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IPC IPC(8): G06N3/08
CPCG06N3/084Y02D10/00
Inventor 詹义强骆佳艳杨坤隆袁斯建郑立荣
Owner FUDAN UNIV