首页> 外文会议>Conference on Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks II; 20040414-20040415; Orlando,FL; US >Smart time-pulse coding photoconverters as basic components 2D-array logic devices for advanced neural networks and optical computers
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Smart time-pulse coding photoconverters as basic components 2D-array logic devices for advanced neural networks and optical computers

机译:智能时间脉冲编码光电转换器作为基本组件2D阵列逻辑设备,用于高级神经网络和光学计算机

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The article deals with a conception of building arithmetic-logic devices (ALD) with a 2D-structure and optical 2D-array inputs-outputs as advanced high-productivity parallel basic operational training modules for realization of basic operation of continuous, neuro-fuzzy, multilevel, threshold and others logics and vector-matrix, vector-tensor procedures in neural networks, that consists in use of time-pulse coding (TPC) architecture and 2D-array smart optoelectronic pulse-width (or pulse-phase) modulators (PWM or PPM) for transformation of input pictures. The input grayscale image is transformed into a group of corresponding short optical pulses or time positions of optical two-level signal swing. We consider optoelectronic implementations of universal (quasi-universal) picture element of two-valued ALD, multi-valued ALD, analog-to-digital converters, multilevel threshold discriminators and we show that 2D-array time-pulse photoconverters are the base elements for these devices. We show simulation results of the time-pulse photoconverters as base components. Considered devices have technical parameters: input optical signals power is 200nW...200μW (if photodiode responsivity is 0.5A/W), conversion time is from tens of microseconds to a millisecond, supply voltage is 1.5... 15V, consumption power is from tens of microwatts to a milliwatt, conversion nonlinearity is less than 1%. One cell consists of 2-3 photodiodes and about ten CMOS transistors. This simplicity of the cells allows to carry out their integration in arrays of 32x32, 64x64 elements and more.
机译:本文讨论了构建具有2D结构和光学2D阵列输入输出的算术逻辑设备(ALD)的概念,作为先进的高生产率并行基本操作训练模块,以实现连续的,神经模糊的基本操作,神经网络中的多级,阈值和其他逻辑以及矢量矩阵,矢量张量程序,其中包括使用时间脉冲编码(TPC)架构和2D阵列智能光电脉宽(或脉冲相位)调制器(PWM)或PPM)来转换输入图片。输入的灰度图像被转换为​​一组相应的短光脉冲或两电平信号摆幅的时间位置。我们考虑了两值ALD,多值ALD,模数转换器,多级阈值鉴别器的通用(准通用)像素的光电实现,并且我们证明了2D阵列时间脉冲光电转换器是用于这些设备。我们将时间脉冲光电转换器的仿真结果显示为基本组件。所考虑的器件具有技术参数:输入光信号功率为200nW ...200μW(如果光电二极管的响应度为0.5A / W),转换时间从数十微秒到毫秒,供电电压为1.5 ... 15V,功耗为从几十微瓦到一毫瓦,转换非线性小于1%。一个单元由2-3个光电二极管和大约10个CMOS晶体管组成。单元的这种简单性允许在32x32、64x64等元素的阵列中进行集成。

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