BYR Achieve · 镜像论坛

在第69期AI文献推荐中，我们为您推荐了图神经网络的热点文献，包括利用GNN量化城市道路网的空间同质性，基于图神经网络对分子表征进行分子对比学习，基于GNN的深度学习框架快速预测共晶体的形成，以及图神经网络的自监督学习综述。本期我们为您选取了4篇文献，介绍人工智能的最新发展前沿，包括用于图像分类的片上光子深度神经网络，基于多层数字编码超表面阵列的可编程衍射深度神经网络，一种基于尖峰神经形态硬件的长短时记忆，适用于多智能任务机器人的时空弹性神经形态计算芯片TianjicX，推送给相关领域的科研人员。文献一用于图像分类的片上光子深度神经网络 An on-chip photonic deep neural network for image classification Ashtiani, Farshid, etc. NATURE, 2022, 606(7914): 501–506 Deep neural networks with applications from computer vision to medical diagnosis are commonly implemented using clock-based processors, in which computation speed is mainly limited by the clock frequency and the memory access time. In the optical domain, despite advances in photonic computation, the lack of scalable on-chip optical non-linearity and the loss of photonic devices limit the scalability of optical deep networks. Here we report an integrated end-to-end photonic deep neural network (PDNN) that performs sub-nanosecond image classification through direct processing of the optical waves impinging on the on-chip pixel array as they propagate through layers of neurons. In each neuron, linear computation is performed optically and the non-linear activation function is realized opto-electronically, allowing a classification time of under 570 ps, which is comparable with a single clock cycle of state-of-the-art digital platforms. A uniformly distributed supply light provides the same per-neuron optical output range, allowing scalability to large-scale PDNNs. Two-class and four-class classification of handwritten letters with accuracies higher than 93.8% and 89.8%, respectively, is demonstrated. Direct, clock-less processing of optical data eliminates analogue-to-digital conversion and the requirement for a large memory module, allowing faster and more energy efficient neural networks for the next generations of deep learning systems. 阅读原文：https://www.nature.com/articles/s41586-022-04714-0 The architecture of the implemented PDNN chip and the structure of an N-input photonic neuron within the PDNN chip 文献二基于多层数字编码超表面阵列的可编程衍射深度神经网络 A programmable diffractive deep neural network based on a digital-coding metasurface array Liu, Che, etc. NATURE ELECTRONICS, 2022, 5(2): 113-122 The development of artificial intelligence is typically focused on computer algorithms and integrated circuits. Recently, all-optical diffractive deep neural networks have been created that are based on passive structures and can perform complicated functions designed by computer-based neural networks. However, once a passive diffractive deep neural network architecture is fabricated, its function is fixed. Here we report a programmable diffractive deep neural network that is based on a multi-layer digital-coding metasurface array. Each meta-atom on the metasurfaces is integrated with two amplifier chips and acts an active artificial neuron, providing a dynamic modulation range of 35 dB (from -22 dB to 13 dB). We show that the system, which we term a programmable artificial intelligence machine, can handle various deep learning tasks for wave sensing, including image classification, mobile communication coding–decoding and real-time multi-beam focusing. We also develop a reinforcement learning algorithm for on-site learning and a discrete optimization algorithm for digital coding. 阅读原文：https://www.nature.com/articles/s41928-022-00719-9 The reprogrammable D2NN platform 文献三一种基于尖峰神经形态硬件的长短时记忆 A Long Short-Term Memory for AI Applications in Spike-based Neuromorphic Hardware Rao, Arjun, etc. NATURE MACHINE INTELLIGENCE, 2022, 4(5): 467-479 Spike-based neuromorphic hardware holds promise for more energy-efficient implementations of deep neural networks (DNNs) than standard hardware such as GPUs. But this requires us to understand how DNNs can be emulated in an event-based sparse firing regime, as otherwise the energy advantage is lost. In particular, DNNs that solve sequence processing tasks typically employ long short-term memory units that are hard to emulate with few spikes. We show that a facet of many biological neurons, slow after-hyperpolarizing currents after each spike, provides an efficient solution. After-hyperpolarizing currents can easily be implemented in neuromorphic hardware that supports multi-compartment neuron models, such as Intel’s Loihi chip. Filter approximation theory explains why after-hyperpolarizing neurons can emulate the function of long short-term memory units. This yields a highly energy-efficient approach to time-series classification. Furthermore, it provides the basis for an energy-efficient implementation of an important class of large DNNs that extract relations between words and sentences in order to answer questions about the text. 阅读原文：https://www.nature.com/articles/s42256-022-00480-w Schematics and dynamics of a two-compartment LIF neuron model with AHP currents 文献四适用于多智能任务机器人的时空弹性神经形态计算芯片TianjicX Neuromorphic computing chip with spatiotemporal elasticity for multi-intelligent-tasking robots Ma, Songchen, etc. SCIENCE ROBOTICS, 2022, 7(67) Recent advances in artificial intelligence have enhanced the abilities of mobile robots in dealing with complex and dynamic scenarios. However, to enable computationally intensive algorithms to be executed locally in multitask robots with low latency and high efficiency, innovations in computing hardware are required. Here, we report TianjicX, a neuromorphic computing hardware that can support true concurrent execution of multiple cross-computing-paradigm neural network (NN) models with various coordination manners for robotics. With spatiotemporal elasticity, TianjicX can support adaptive allocation of computing resources and scheduling of execution time for each task. Key to this approach is a high-level model, “Rivulet,” which bridges the gap between robotic-level requirements and hardware implementations. It abstracts the execution of NN tasks through distribution of static data and streaming of dynamic data to form the basic activity context, adopts time and space slices to achieve elastic resource allocation for each activity, and performs configurable hybrid synchronous-asynchronous grouping. Thereby, Rivulet is capable of supporting independent and interactive execution. Building on Rivulet with hardware design for realizing spatiotemporal elasticity, a 28-nanometer TianjicX neuromorphic chip with event-driven, high parallelism, low latency, and low power was developed. Using a single TianjicX chip and a specially developed compiler stack, we built a multi-intelligent-tasking mobile robot, Tianjicat, to perform a cat-and-mouse game. Multiple tasks, including sound recognition and tracking, object recognition, obstacle avoidance, and decision-making, can be concurrently executed. Compared with NVIDIA Jetson TX2, latency is substantially reduced by 79.09 times, and dynamic power is reduced by 50.66%. 阅读原文：https://www.science.org/doi/10.1126/scirobotics.abk2948

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