Abstract
1. INTRODUCTION
Implementation of neuromorphic photonics on a silicon photonic integrated chip is gradually becoming a promising technology for deep learning accelerators, which utilizes photonic processors to function as artificial intelligence (AI) cores [1–4]. The realization and advancement of integrated programmable photonic processors [5–13] provide a feasible strategy for the construction of optical neural networks (ONNs) [14,15]. Compared to electronics, neuromorphic photonics has advantages of well-known high-bandwidth, and ultralow energy consumption due to negligible energy for light propagation with encoded information [16]. With the rapid advancement of the complementary metal–oxide–semiconductor (CMOS)-compatible silicon-on-insulator (SOI) platform [17–19], integrated silicon waveguides [20] and optical modulators such as Mach–Zehnder interferometers (MZIs) [21–26] and micro-ring resonators (MRRs) [27–29] can be easily formed as programmable processors for the construction of integrated ONNs [21,22,30,31] and other similar deep learning networks such as convolutional neural networks (CNNs) [6,32,33] and recurrent neural networks (RNNs) [2,34].
However, there remain challenges in the precise control of device performance and achieving excellent uniformity for various components in neuromorphic photonic chips. For example, there is a 15% reduction of vowel classification accuracy with the nanophotonic processor [22] and limited accuracy (about 88%) in handwriting image recognition using the photonic CNN chip [33]. The major problem is non-ideal photonic components, which leads to uncertain performance of the required functionality. In previous research, a few optimization procedures have been reported aiming at restoring the fidelity of the unitary matrix by using numerical initialization of parameters in MZIs [35,36]. However, these strategies mainly focused on the fidelity of the implemented unitary matrix instead of the desired functionality of the ONN. Hence, the effects of imprecise components could be underestimated. Also, these optimizations require precise characterization of each device separately and are actualized after fabrication, leading to extra computational power consumption and suffering from scalability problems in mass production.
Other methods adopted physical architecture modification to mitigate the effects of imprecisions. A double MZI configuration was proposed to compensate for fabricated MZIs with imperfect splitting ratios without calibration [37]. Shokraneh et al. [38] reported a diamond mesh of MZIs, which forms a symmetrical architecture to resist imprecisions in the ONN, and Fang
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To address the above-mentioned issues, in this paper, we propose a two-step
We perform the training scheme in a feedforward photonic neural network implemented by the mesh of MZIs with tunable thermo-optic phase shifters and demonstrate its effectiveness in practical learning tasks, including
2. ONN ARCHITECTURE AND TRAINING SCHEME
A. Constructions of ONN
The typical ONN is a feedforward sequential processing flow comprising an input layer with artificial neurons, a series of hidden layers, activation layers, and an output layer, as shown in Fig. 1(a). The continuous wave laser source and optical amplifier generate an optical signal and split it into different waveguide channels. The input image is encoded into the optical signal in the form of using optical attenuators and modulators, where and are the amplitude and phase of the signal, respectively. Then, signals go through the optical interference unit (OIU) and optical nonlinear unit (ONU). After propagating in the network, optical output signals are converted to electrical signals by using photodetectors for the subsequent information storage and processing. Here we use an
Figure 1.(a) Illustration of artificial neural network (ANN) architecture for image recognition implemented by photonic units, including optical input encoding parts, optical interference units, optical nonlinear units, and photodetectors. (b) Demonstration of a programmable Mach–Zehnder interferometer consisting of directional couplers and thermo-optical phase shifters.
The paramount part in the ONN to function as the synaptic weight is the OIU, which can realize the multiply–accumulate operation (MAC) of input optical signals depicted in Fig. 1(a). It constructs a programmable MAC block from input modes to output modes. The operation block can be decomposed into a mesh of MZIs, as demonstrated in Fig. 1(b). Each MZI consists of two phase shifters parameterized and two 50:50 directional couplers. The modulation of phase shifters is implemented by tuning the temperature of the rib waveguides according to the thermal-optic effect [46]. Consequently, the overall scattering matrix of each MZI is derived as
B. Quantized Parameter Imprecisions
Since there are severe impacts of parameter imprecisions, the distorted scattering matrix of the MZI caused by these errors is consequently obtained. There are four main types of imprecisions in devices, including phase shift error, insertion loss, drift of the coupling coefficient, and photodetection noise. From previous experimental measurements [22], the phase errors can be modeled as random Gaussian distributed variables where the expectation is zero, and the standard deviation is typically in the range of 0.05 rad. To have a more precise analysis of the parameters , we have to consider the source of phase errors. The source can be divided into two parts. The first one can be treated as phase variation caused by thermal effects from neighboring phase shifters [52,53]. The th affected phase shifter can be calculated by the adjacent phase shifters , defined by . is the thermal effect coefficient from the th shifter to the th shifter, which is determined by the distance between two phase shifters, and is the phase shift variation of the surrounded th shifter. The coefficient can be derived from the heat conduction equation [52] or measured from experiments [53]. Since is about 0.065 with a distance of 80 μm, the term can be ignored. The other variation source is from the fabrication imperfection and digital to analog converter (DAC) limited precision. They can be regarded as standard deviations and , respectively. In this chip [22], an individual MZI has a far lower noise value of rad. If a 12-bit DAC is used, the DAC resolution is about for 5 V voltage and , where is the voltage–phase conversion coefficient, typically about 1.6 rad/V. The overall standard deviation is lower than .
Another error in the MZI is insertion loss . It can be assumed as a constant attenuation coefficient for each MZI. The perturbed scattering matrix of the MZI array is characterized as for the case where the number of MZIs is and the insertion loss . Also, the imprecise width of the waveguide could change the coupling region of directional couplers, causing the coupling ratio error [36]. This error can be calculated by measuring the extinction ratio of the MZI in the crossbar state, where . In summary, the scattering matrix of the MZI with errors is expressed as
Typically, after the optical–electrical conversion, there is photodetection noise , experimentally following Gaussian distribution . The practical received output with imprecisions is expressed as where is the ideal output vector of the th sample at the output layer. Since the sources of these noises are different and independent of each other, these noises are considered as independent variables and are taken into account simultaneously when training the noisy ONN.
There are also other potential drifts existing in experiments, including optical input encoding, device aging, temperature, and learning duration. In the practical MZI-based ONN chip [21,22,31], input signals are encoded by cascaded MZIs for input ports, and these input signals can be inferred from the other port of the MZI. Hence, the effect of optical input drifts can be minimized by real-time monitoring. Device aging mainly exists in electric-optic phase shifters where slab Si waveguides are doped to form PN junctions [54]. As mentioned before, we adopt thermal phase shifters, which typically use TiN as heaters to modulate phases. Hence, device aging can be minimized. The temperature drift can be controlled by a thermoelectric controller to ensure long-term temperature stability better than 0.01 K in experiments. As the GA training is conducted on computers, the computations in the
By using the quantized parameter imprecisions, the degradation of ONN performance can be directly evaluated on the basis of the network’s accuracy in the specific dataset. Distorted scattering matrices of the MZI array were set as synaptic weights in hidden layers. Then the imprecise ONN was applied to perform a machine learning task in the supervised learning way. Classification accuracy of the affected ONN in different error ranges was obtained as depicted in Fig. 2. Figure 2(a) demonstrates the accuracy degradation caused by phase shift error and MZI loss. The typical phase shift error is about 0.05 rad, which lowers the classification accuracy by about 4%. For silicon photonics, the loss of each MZI is about 0.05–0.1 dB, which indicates that the accuracy would drop by about 1%. Figures 2(b) and 2(c) indicate the impacts of the extinction ratio and photodetection noise on accuracy, respectively. The extinction ratio of experimentally measured MZI can reach 20 dB and photodetection noise about 0.05, reducing the accuracy by about 11% and 0.7%, respectively. This indicates that in practical cases, the coupling ratio error would contribute more to accuracy degradation than MZI loss or photodetection noise.
Figure 2.Heat map of classification accuracy in the MNIST dataset with the imprecise ONN chip. (a) Classification performance between phase shift error
C. Workflow of the Training Scheme
Without any calibration steps or extra imprecise network training, the ONN is typically sensitive to parameter imprecisions, hindering the use of photonic chips in machine learning. Here we propose a network training scheme using GA training considering practical imprecisions existing in optical components. The training flow is illustrated in Fig. 3. First, neural synaptic weights in the ONN are iterated and trained by using the gradient stochastic descent algorithm. Based on the backpropagation of loss, the classification accuracy can rapidly converge to the maximum, and the optimal phase shifts of MZIs are obtained. Then, we consider the effect of parameter imprecisions, and the GA is applied for the optimization of neuron weights learning. The optimal phase shifts are set as references. Compensatory phase shifts are added on , and the compensated phase shift array is defined as an individual in the GA process. The compensated phase shift range of is based on the phase shift error standard deviation . Randomly generating compensated phase shifts, individuals are achieved as the initial population in the GA training stage, where is the number of individuals in one generation. Each individual will produce a diverse ONN. Then we define average classification accuracy in imprecise chips with parameter imprecisions as the fitness function , where , and means the output vector of the individual. To ensure that the numerical optimization can offer a significant improvement on practical hardware, we define the fitness in GA as the average accuracy in a large number of ONNs with randomly sampled noises. In the GA, several operators are applied, including selection, crossover, and mutation [55]. Roulette method selection is adopted in the selection stage, meaning that the individual with higher fitness is more likely to be chosen. Also, an individual can be selected repeatedly in one evolution stage. By continuously generating new individuals, the optimum individual can ensure the average accuracy to be close to the ideal one by compensating for all imprecisions. In this way, the genetics-based trained ONN is shown to have enhanced robustness in erroneous cases.
Figure 3.Training flow of the ONN with parameter imprecisions using the genetic algorithm. Two major stages are involved and illustrated, including gradient training of the ideal ONN and genetic training in the imprecise chips.
3. SIMULATION, RESULTS, AND DISCUSSION
A. Software Implementation
Two types of datasets are chosen to validate our training scheme. One is the
Figure 4.Training curves of the ideal ONN using gradient descent algorithm in (a)
Since GA is a heuristic method to generate high-quality solutions to search problems by relying on bio-inspired operators, it strongly relies on initial individuals. Hence, the adoption of the gradient descent algorithm in the first training of the ideal ONN helps to find optimal individuals so that the training based on GA would quickly converge to global optima instead of local optima. As shown in Fig. 4(e), the two-step training method is faster to converge than only GA training. In addition, we analyze the reason for the different standard deviations of the accuracy distribution in Fig. 4(d). Figure 4(f) compares the standard deviations in different numbers of layers and layer widths. The results show that a greater number of layers and larger layer widths lead to larger standard deviations. A more complex ONN would lead to more significant changes in accuracy, while the difference in accuracy distribution in Fig. 4(d) is mainly related to the type of dataset.
B. Analysis of Hyper-Parameters
The dominant factor in the GA training scheme is parameter imprecision range. It determines the degradation of ONN performance since larger ranges of imprecisions obviously increase the randomization of the network’s functionality. Hence, it is necessary to survey the training scheme in different imprecision ranges. We compare the scheme in two types of imprecision ranges as defined below.
The first case is the typical error ranges that we took from this chip [22] to test the validation of GA training in previous sessions. By applying a more precise phase shift error model , the standard deviation of phase shift errors can be remarkably reduced by about eight times as compared to that reported in Ref. [22]. Considering the fact that the network is
Figure 5.(a) Accuracy training curves in the MNIST dataset during the GA training stage in the condition of typical error ranges
Since the training scheme is a pure software method, various hyper-parameters in GA training make a significant impact on the overall robustness of the ONN. Therefore, we analyze the effects of these hyper-parameters containing the compensated phase shift range , the number of imprecise chips , and the population in each generation. To illustrate the effects of these hyper-parameters more universally, we use the MNIST dataset and the typical error ranges for demonstration. Figure 6(a) demonstrates the training curves in the condition of different compensated phase shift ranges, where smaller ranges have more severe impacts on the training results. In Fig. 6(b), it can be observed that when the compensated phase shift range is , the average accuracy after GA training can achieve the maximum, and the best accuracy distribution is obtained. It is noted that if , the average accuracy after training is only about 73%, which is much less than in other ranges, indicating that if the compensated phase shift range is too small, the GA training scheme cannot search out the individual with high robustness in a small solution space. Also, if the compensated phase shift range is larger than , such as , it would increase the uncertainty of the obtained solutions, leading to a reduction in average accuracy.
Figure 6.(a) Accuracy training curves in the GA training stage in different compensated phase shift ranges
When we change the number of imprecise chips, the number of imprecise chips used to evaluate the fitness of the individual has a smaller impact on the accuracy distribution than the compensated phase shift range. As shown in Fig. 7(a), for different numbers of chips, the training curves converge to the same results. Also, Fig. 7(b) shows a similar accuracy distribution in different numbers of chips, indicating that 30 imprecise chips are sufficient to estimate the robustness of the individual in these error ranges. The irrelevance between the number of chips and classification accuracy can significantly enhance the computational efficiency in the GA training step. Regarding the effects of different populations in each generation, as depicted in Fig. 8, the curves point out that increasing the number of individuals impressively enhances the maximum accuracy. However, the computation time also increases exponentially as the population rises. Figure 8(a) reports that more individuals can converge to the optima more quickly. The average value of the accuracy distribution in Fig. 8(b) tends to saturate when the population increases to 90, suggesting that the number of individuals in the range of 70–90 is sufficient and is a good balance between computation cost and the improved robustness of the ONN chip.
Figure 7.(a) Accuracy training curves in the GA training stage using different numbers of imprecise chips
Figure 8.(a) Accuracy training curves in the GA training stage in the condition of different populations. (b) Effects of different populations in evolution on the accuracy distribution in imprecise chips.
C. Comparison to SA and PSO
In the self-learning process of weights in ONNs, there are also alternative approaches that can replace the GA to train neurons based on similar evolutionary algorithms, such as simulated annealing (SA) and particle swarm optimization (PSO) [60]. However, the training process of ONNs has the feature of multiple variables updating, which can restrain the convergence speed and training performance of the algorithms. To demonstrate the efficiency of the GA in this situation, we implement these three algorithms in the same conditions and evaluate their performance in imprecise chips. Because the MNIST dataset requires more neurons and layers than
Figure 9.(a) Accuracy training curves in three heuristic algorithms with the accuracy converging to a particular value. (b) Accuracy distribution of three algorithms in the same imprecise chips.
4. CONCLUSION
To sum up, we propose and demonstrate a two-step
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