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Register a new userPhotonic Convolution Neural Network Based on Interleaved Time-Wavelength Modulation
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Convolution neural network (CNN), as one of the most powerful and popular technologies, has achieved remarkable progress for image and video classification since its invention in 1989. However, with the high definition video-data explosion, convolution layers in the CNN architecture will occupy a great amount of computing time and memory resources due to high computation complexity of matrix multiply accumulate operation. In this paper, a novel integrated photonic CNN is proposed based on double correlation operations through interleaved time-wavelength modulation. Micro-ring based multi-wavelength manipulation and single dispersion medium are utilized to realize convolution operation and replace the conventional optical delay lines. 200 images are tested in MNIST datasets with accuracy of 85.5% in our photonic CNN versus 86.5% in 64-bit computer.We also analyze the computing error of photonic CNN caused by various micro-ring parameters, operation baud rates and the characteristics of micro-ring weighting bank. Furthermore, a tensor processing unit based on 4x4 mesh with 1.2 TOPS (operation per second when 100% utilization) computing capability at 20G baud rate is proposed and analyzed to form a paralleled photonic CNN.
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Deep neural networks with applications from computer vision and image processing to medical diagnosis are commonly implemented using clock-based processors, where computation speed is limited by the clock frequency and the memory access time. Advances in photonic integrated circuits have enabled research in photonic computation, where, despite excellent features such as fast linear computation, no integrated photonic deep network has been demonstrated to date due to the lack of scalable nonlinear functionality and the loss of photonic devices, making scalability to a large number of layers challenging. Here we report the first integrated end-to-end photonic deep neural network (PDNN) that performs instantaneous image classification through direct processing of optical waves. Images are formed on the input pixels and optical waves are coupled into nanophotonic waveguides and processed as the light propagates through layers of neurons on-chip. Each neuron generates an optical output from input optical signals, where linear computation is performed optically and the nonlinear activation function is realised opto-electronically. The output of a laser coupled into the chip is uniformly distributed among all neurons within the network providing the same per-neuron supply light. Thus, all neurons have the same optical output range enabling scalability to deep networks with large number of layers. The PDNN chip is used for 2- and 4-class classification of handwritten letters achieving accuracies of higher than 93.7% and 90.3%, respectively, with a computation time less than one clock cycle of state-of-the-art digital computation platforms. Direct clock-less processing of optical data eliminates photo-detection, A/D conversion, and the requirement for a large memory module, enabling significantly faster and more energy-efficient neural networks for the next generations of deep learning systems.
As artificial neural networks (ANNs) continue to make strides in wide-ranging and diverse fields of technology, the search for more efficient hardware implementations beyond conventional electronics is gaining traction. In particular, optical implementations potentially offer extraordinary gains in terms of speed and reduced energy consumption due to intrinsic parallelism of free-space optics. At the same time, a physical nonlinearity, a crucial ingredient of an ANN, is not easy to realize in free-space optics, which restricts the potential of this platform. This problem is further exacerbated by the need to perform the nonlinear activation also in parallel for each data point to preserve the benefit of linear free-space optics. Here, we present a free-space optical ANN with diffraction-based linear weight summation and nonlinear activation enabled by the saturable absorption of thermal atoms. We demonstrate, via both simulation and experiment, image classification of handwritten digits using only a single layer and observed 6-percent improvement in classification accuracy due to the optical nonlinearity compared to a linear model. Our platform preserves the massive parallelism of free-space optics even with physical nonlinearity, and thus opens the way for novel designs and wider deployment of optical ANNs.
Neural networks have enabled applications in artificial intelligence through machine learning, and neuromorphic computing. Software implementations of neural networks on conventional computers that have separate memory and processor (and that operate sequentially) are limited in speed and energy efficiency. Neuromorphic engineering aims to build processors in which hardware mimics neurons and synapses in the brain for distributed and parallel processing. Neuromorphic engineering enabled by photonics (optical physics) can offer sub-nanosecond latencies and high bandwidth with low energies to extend the domain of artificial intelligence and neuromorphic computing applications to machine learning acceleration, nonlinear programming, intelligent signal processing, etc. Photonic neural networks have been demonstrated on integrated platforms and free-space optics depending on the class of applications being targeted. Here, we discuss the prospects and demonstrated applications of these photonic neural networks.
We characterize the frequency response of channel-interleaved photonic analog-to-digital converters (CI-PADCs) theoretically and experimentally. The CI-PADC is composed of a photonic frontend for photonic sampling and an electronic backend for quantization. The photonic frontend includes a photonic sampling pulse generator for directly high-speed sampling and an optical time-division demultiplexer (OTDM) for channel demultiplexing. It is found that the frequency response of the CI-PADC is influenced by both the photonic sampling pulses and the OTDM, of which the combined impact can be characterized through demultiplexed pulse trains. First, the frequency response can be divided into multiple frequency intervals and the range of the frequency interval equals the repetition rate of demultiplexed pulse trains. Second, the analog bandwidth of the CI-PADC is determined by the optical spectral bandwidth of demultiplexed pulse trains which is broadened in the OTDM. Further, the effect of the OTDM is essential for enlarging the analog bandwidth of the CI-PADC employing the photonic sampling pulses with a limited optical spectral bandwidth.
Digital accelerators in the latest generation of CMOS processes support multiply and accumulate (MAC) operations at energy efficiencies spanning 10-to-100~fJ/Op. But the operating speed for such MAC operations are often limited to a few hundreds of MHz. Optical or optoelectronic MAC operations on todays SOI-based silicon photonic integrated circuit platforms can be realized at a speed of tens of GHz, leading to much lower latency and higher throughput. In this paper, we study the energy efficiency of integrated silicon photonic MAC circuits based on Mach-Zehnder modulators and microring resonators. We describe the bounds on energy efficiency and scaling limits for N x N optical networks with todays technology, based on the optical and electrical link budget. We also describe research directions that can overcome the current limitations. The next generation of silicon photonics, which we term Silicon Photonics 2.0, promises many exciting opportunities to enable the next frontier in optical computing and communication.
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