Retinomorphic systems that can see, recognize, and respond to real-time environmental information will extend the complexity and range of tasks that an exoskeleton robot can perform to better assist physically disabled people. However, the lack of ultrasensitive, reconfigurable, and large-scale integratable retinomorphic devices and advanced edge-processing algorithms makes it difficult to realize retinomorphic hardware. Here, we report the retinomorphic hardware prototype with a 4096-pixel perovskite image sensor array as core module to endow embodied intelligent vision functionalities. The retinomorphic sensor array, using a one photodetector–one transistor geometry to resemble retinal circuit with broadband, ultrahigh, and reconfigurable photoresponsivities, executes both adaptive imaging with a contrast enhancement of ~620% under a dim-lit intensity of 10 microwatts per square centimeter and an instantaneous one-dimensional feature extraction algorithm to decompose the origin visual scenarios into parsimoniously encoded spatiotemporal information. This retinomorphic system endows embodied intelligence with adaptive imaging, in situ processing, and decision-making capabilities and promises enormous potential for autonomous robot applications.

Quantum dot cellular automata (QCA) is considered one of the most promising technologies to replace the current CMOS technology. Compared with the traditional transistor technology, the computation relies on a new paradigm based on the interaction between nearby QCA cells. It has significant advantages, such as operating frequency (THz), high device density, and low power consumption. In this paper, a novel XOR/XNOR-function logic gate with two inputs, two enable inputs and one output is proposed and designed in Quantum-dot Cellular Automata (QCA) nanotechnology. In order to demonstrate the functionality and capabilities of the proposed QCAbased XOR/XNOR architecture, performance is evaluated and analyzed. The proposed XOR/XNOR logic gate has a superb performance in terms of area, complexity, power consumption and cost function in comparison to some existing QCA-based XOR architectures. Moreover, some efficient circuits based on the proposed XOR/XNOR gate are designed in QCA.

Quantum-dot cellular automata (QCA) is an alternative nanotechnology to traditional CMOS technology. In theory, circuits in QCA have the advantages of lower power consumption, higher integration density and switching frequency than circuits based on CMOS. Random access memory (RAM) circuits that are critical in designing large memory circuits have been explored. In this brief, a novel loop-based RAM cell with asynchronous set and reset, by using a new 2-1 multiplexer and D-latch, is proposed. Efficient 1×4 RAM and 4×4 RAM with three-layer structure are constructed. The proposed RAM cell has 35% improvement in both circuit footprint and cost, compared with an existing best scheme. The 1×4 RAM has less cells, delay and area than the previous designs with the same circuit scheme. The 4×4 RAM and m×n RAM show that the proposed RAM cell has excellent scalability for designing complex circuits. Functional correctness of the proposed designs has been proved by using QCADesigner 2.0.3.

In background subtraction tasks, spatial and temporal contexts are beneficial in detecting moving objects. The methods based on Deep Neural Networks in this task has explored different topologies, which are composed of the conventional operations of convolutional neural networks, such as Convolutional Long-short Term Memory layer (ConvLSTM), 2D convolutional layer, or 3D convolutional layer, to capture these contexts. In this work, we propose a new background subtraction algorithm named spatial–temporal propagation network. An end-to-end network with novel layers, whose process of operation is equivalent to that the feature maps multiply with affinity matrices, is proposed to capture the spatial–temporal correlation in video sequences and aggregate the deep features from the consecutive frames. Experimental results on CDnet-2014 and LASIESTA datasets show that this novel layer provides an alternative way for our network to aggregate multiscale spatial–temporal features. Meanwhile, the proposed network achieves state-of-the-art performance and is generalizable to unseen videos.