Analog spatiotemporal feature extraction for cognitive radio-frequency sensing with integrated photonics.

Analog feature extraction (AFE) is an appealing strategy for low-latency and efficient cognitive sensing systems since key features are much sparser than the Nyquist-sampled data. However, applying AFE to broadband radio-frequency (RF) scenarios is challenging due to the bandwidth and programmability bottlenecks of analog electronic circuitry. Here, we introduce a photonics-based scheme that extracts spatiotemporal features from broadband RF signals in the analog domain. The feature extractor structure inspired by convolutional neural networks is implemented on integrated photonic circuits to process RF signals from multiple antennas, extracting valid features from both temporal and spatial dimensions. Because of the tunability of the photonic devices, the photonic spatiotemporal feature extractor is trainable, which enhances the validity of the extracted features. Moreover, a digital-analog-hybrid transfer learning method is proposed for the effective and low-cost training of the photonic feature extractor. To validate our scheme, we demonstrate a radar target recognition task with a 4-GHz instantaneous bandwidth. Experimental results indicate that the photonic analog feature extractor tackles broadband RF signals and reduces the sampling rate of analog-to-digital converters to 1/4 of the Nyquist sampling while maintaining a high target recognition accuracy of 97.5%. Our scheme offers a promising path for exploiting the AFE strategy in the realm of cognitive RF sensing, with the potential to contribute to the efficient signal processing involved in applications such as autonomous driving, robotics, and smart factories.

Low-threshold all-optical nonlinear activation function based on injection locking in distributed feedback laser diodes.

We experimentally demonstrate an all-optical nonlinear activation unit based on the injection-locking effect of distributed feedback laser diodes (DFB-LDs). The nonlinear carrier dynamics in the unit generates a low-threshold nonlinear activation function with optimized operating conditions. The unit can operate at a low threshold of -15.86 dBm and a high speed of 1 GHz, making it competitive among existing optical nonlinear activation approaches. We apply the unit to a neural network task of solving the second-order ordinary differential equation. The fitting error is as low as 0.0034, verifying the feasibility of our optical nonlinear activation approach. Given that the large-scale fan-out of optical neural networks (ONNs) will significantly reduce the optical power in one channel, our low-threshold scheme is suitable for the development of high-throughput ONNs.

Photonic parallel channel estimation of MIMO-OFDM wireless communication systems.

Channel estimation is a key technology in MIMO-OFDM wireless communication systems. Increasingly extensive application scenarios and exponentially growing data volumes of MIMO-OFDM systems have imposed greater challenges on the speed, latency, and parallelism of channel estimation based on electronic processors. Here, we propose a photonic parallel channel estimation (PPCE) architecture which features radio-frequency direct processing. Proof-of-concept experiment is carried out to demonstrate the general feasibility of the proposed architecture at different frequency bands (100 MHz, 4 GHz, and 10 GHz). The mean square errors (MSEs) between the experimental channel estimation results and the theoretically simulated ones lie on the order of 10-3. The bit error rates (BERs) are below the pre-forward error correction (pre-FEC) threshold. Besides, we analyze the performance of PPCE under different signal-to-noise ratios (SNRs), baseband symbol forms, and weight tuning precisions. The proposed PPCE architecture has the potential to achieve high-speed, highly parallel channel estimation in large-scale MIMO-OFDM systems after the photonic-electronic chip integration.

Enhancement of calculation accuracy of the integrated photonic tensor flow processer by global optical power allocation.

We present a global optical power allocation architecture, which can enhance the calculation accuracy of the integrated photonic tensor flow processor (PTFP). By adjusting the optical power splitting ratio according to the weight value and loss of each calculating unit, this architecture can efficiently use optical power so that the signal-to-noise ratio of the PTFP is enhanced. In the case of considering the on-chip optical delay line and spectral loss, the calculation accuracy measured in the experiment is enhanced by more than 1 bit compared with the fixed optical power allocation architecture.

High-order tensor flow processing using integrated photonic circuits.

Tensor analytics lays the mathematical basis for the prosperous promotion of multiway signal processing. To increase computing throughput, mainstream processors transform tensor convolutions into matrix multiplications to enhance the parallelism of computing. However, such order-reducing transformation produces data duplicates and consumes additional memory. Here, we propose an integrated photonic tensor flow processor (PTFP) without digitally duplicating the input data. It outputs the convolved tensor as the input tensor 'flows' through the processor. The hybrid manipulation of optical wavelengths, space dimensions, and time delay steps, enables the direct representation and processing of high-order tensors in the optical domain. In the proof-of-concept experiment, an integrated processor manipulating wavelengths and delay steps is implemented for demonstrating the key functionalities of PTFP. The multi-channel images and videos are processed at the modulation rate of 20 Gbaud. A convolutional neural network for video action recognition is demonstrated on the processor, which achieves an accuracy of 97.9%.

Parallel optical coherent dot-product architecture for large-scale matrix multiplication with compatibility for diverse phase shifters.

Photonics physically promises high-speed and low-consumption computing of matrix multiplication. Nevertheless, conventional approaches are challenging to achieve large throughput, high precision, low power consumption, and high density simultaneously in a single architecture, because the integration scale of conventional approaches is strongly limited by the insertion loss of cascaded optical phase shifters. Here, we present a parallel optical coherent dot-product (P-OCD) architecture, which deploys phase shifters in a fully parallel way. The insertion loss of phase shifters does not accumulate at large integration scale. The architecture decouples the integration scale and phase shifter insertion loss, making it possible to achieve superior throughput, precision, energy-efficiency, and compactness simultaneously in a single architecture. As the architecture is compatible with diverse integration technologies, high-performance computing can be realized with various off-the-shelf photonic phase shifters. Simulations show that compared with conventional architectures, the parallel architecture can achieve near 100× higher throughput and near 10× higher energy efficiency especially with lossy phase shifters. The parallel architecture is expected to perform its unique advantage in computing-intense applications including AI, communications, and autonomous driving.

Multi-band low-noise microwave-signal-receiving system with a photonic frequency down-conversion and transfer-learning network.

In this Letter, we propose and demonstrate a multi-band signal-receiving system, powered by photonic frequency down-conversion and transfer learning. A photonic frequency down-conversion system directly receives the microwave signals, and the transfer-learning network (TLN) lowers the noise in the signals. In addition to the effectiveness of denoising, the TLN also features ultra-fast retraining for signals of different types or different multi-band frequencies. Experimental results showed that the proposed microwave-signal-receiving system can improve the signal-to-noise (SNR) ratio of signals of different types, SNR, and duty cycles. For network retraining, the TLN requires only three times less data and 10 times less time consumption than conventional training methods.

Optical coherent dot-product chip for sophisticated deep learning regression.

Optical implementations of neural networks (ONNs) herald the next-generation high-speed and energy-efficient deep learning computing by harnessing the technical advantages of large bandwidth and high parallelism of optics. However, due to the problems of the incomplete numerical domain, limited hardware scale, or inadequate numerical accuracy, the majority of existing ONNs were studied for basic classification tasks. Given that regression is a fundamental form of deep learning and accounts for a large part of current artificial intelligence applications, it is necessary to master deep learning regression for further development and deployment of ONNs. Here, we demonstrate a silicon-based optical coherent dot-product chip (OCDC) capable of completing deep learning regression tasks. The OCDC adopts optical fields to carry out operations in the complete real-value domain instead of in only the positive domain. Via reusing, a single chip conducts matrix multiplications and convolutions in neural networks of any complexity. Also, hardware deviations are compensated via in-situ backpropagation control provided the simplicity of chip architecture. Therefore, the OCDC meets the requirements for sophisticated regression tasks and we successfully demonstrate a representative neural network, the AUTOMAP (a cutting-edge neural network model for image reconstruction). The quality of reconstructed images by the OCDC and a 32-bit digital computer is comparable. To the best of our knowledge, there is no precedent of performing such state-of-the-art regression tasks on ONN chips. It is anticipated that the OCDC can promote the novel accomplishment of ONNs in modern AI applications including autonomous driving, natural language processing, and scientific study.

Active control of acoustics-caused nano-vibration in atomic force microscope imaging.

In this paper, we propose a finite-impulse-response (FIR)-based feedforward control approach to mitigate the acoustic-caused probe vibration during atomic force microscope (AFM) imaging. Compensation for the acoustic-caused probe vibration is important, as environmental disturbances including acoustic noise induce nano-scale probe vibration, directly affecting the AFM performance in applications such as imaging, nanomechanical characterization, and nanomanipulation. Although conventional passive noise cancellation apparatus has been employed, limitation exists, and residual noise still persists. Thus, a FIR-based active feedforward control approach is developed, by exploring a data-driven approach to account for the vibrational dynamics of the probe caused by the environmental acoustic noise in the controller design. An experimental implementation in AFM imaging application is presented and discussed to illustrate the proposed technique. Experimental results show that the FIR-based feedforward control is promising to not only complement, but also alleviate the limitations of passive noise control in AFM operations.