Abstract: A layout method for a scalable multi-die network-on-chip FPGA architecture is provided. An application of the aforementioned layout method for the scalable multi-die network-on-chip FPGA architecture is further provided. A scalable multi-die FPGA architecture based on network-on-chip and a corresponding hierarchical recursive layout algorithm are provided, aiming to directly map a register transfer level dataflow design generated by existing high-level synthesis onto the provided interconnection architecture. The layout method can exploit the potential for hierarchical topology and make more efficient use of dedicated interconnection resources, such as cross-die nets, network-on-chips, and high-speed transceivers.

Abstract: A material fabrication method comprises (a) fabricating a structure from a programmable amyloid material (PAM) ink comprising an amyloid monomer stabilized in a liquid solvent; and (b) contacting the structure with an agent which triggers polymerization of the amyloid monomer and stabilization of the structure.

Abstract: A dual-six-transistor (D6T) in-memory computing (IMC) accelerator supporting always-linear discharge and reducing digital steps is provided. In the IMC accelerator, three effective techniques are proposed: (1) A D6T bitcell can reliably run at 0.4 V and enter a standby mode at 0.26 V, to support parallel processing of dual decoupled ports. (2) An always-linear discharge and convolution mechanism (ALDCM) not only reduces a voltage of a bit line (BL), but also keeps linear calculation throughout an entire voltage range of the BL. (3) A bypass of a bias voltage time converter (BVTC) reduces digital steps, but still keeps high energy efficiency and computing density at a low voltage. A measurement result of the IMC accelerator shows that the IMC accelerator achieves an average energy efficiency of 8918 TOPS/W (8b×8b), and an average computing density of 38.6 TOPS/mm2 (8b×8b) in a 55 nm CMOS technology.

Abstract: An energy-efficient point cloud feature extraction method based on a field-programmable gate array (FPGA) is mapped onto the FPGA for running. The energy-efficient point cloud feature extraction method based on the FPGA is applied to point cloud feature extraction in unmanned driving; or an intelligent robot. Compared with an existing technical solution, the energy-efficient point cloud feature extraction method based on the FPGA has following innovative points: a low-complexity projection method for organizing unordered and sparse point clouds, a high-parallel method for extracting a coarse-grained feature point, and a high-parallel method for selecting a fine-grained feature point.

Abstract: The present disclosure relates to an in-situ temperature control platform, including an independent sample holder, a sample holder fixing cartridge, a customized sample stage and an anode contact pin. The independent sample holder includes a sample loading spot and a sample holder grip. The sample holder fixing cartridge includes a fixing cartridge body, the fixing cartridge body is provided with a sample holder slot, the bottom surface of the sample holder slot is provided with a heating element slot, and the sample holder slot is aligned with the sample loading spot. The bottom surface of the heating element slot is provided with a heating element fixing pinhole. The customized sample stage includes a sample stage body, the sample stage body is provided with a heating element support, and the heating element support is provided with a heating element.

Abstract: A max-flow/min-cut solution algorithm for early terminating a push-relabel algorithm is provided. The max-flow/min-cut solution algorithm is used for an application that does not require an exact maximum flow, and includes: defining an early termination condition of the push-relabel algorithm by a separation condition and a stable condition; determining that the separation condition is satisfied if there is no source node s, s?S, in the set T at any time in an operation process of the push-relabel algorithm; determining that the stable condition is satisfied if there is no active node in the set T; and terminating the push-relabel algorithm if both the separation condition and the stability condition are satisfied. The early termination technique is proposed to greatly reduce redundant computations and ensure that the algorithm terminates correctly in all cases.

Abstract: An image rendering system comprising a preprocessing unit coupled to a feature extract unit and a color rendering unit over a data bus. The preprocessing unit generates vector representations of spatial coordinates of sample points along camera rays corresponding to pixels of an image to be rendered. The feature extract unit generates a feature map of the image based on the vector representations, color and intensity values of the sample point through a first machine learning model. The color rendering unit renders the image based on the feature map through a second machine learning model. The first machine learning model is different from the second machine learning model.

Abstract: A pure integer quantization method for a lightweight neural network (LNN) is provided. The method includes the following steps: acquiring a maximum value of each pixel in each of the channels of the feature map of a current layer; dividing a value of each pixel in each of the channels of the feature map by a t-th power of the maximum value, t?[0,1]; multiplying a weight in each of the channels by the maximum value of each pixel in each of the channels of the corresponding feature map; and convolving the processed feature map with the processed weight to acquire the feature map of a next layer. The algorithm is verified on SkyNet and MobileNet respectively, and lossless INT8 quantization on SkyNet and maximum quantization accuracy so far on MobileNetv2 are achieved.

Abstract: A ripple push method for a graph cut includes: obtaining an excess flow ef(v) of a current node v; traversing four edges connecting the current node v in top, bottom, left and right directions, and determining whether each of the four edges is a pushable edge; calculating, according to different weight functions, a maximum push value of each of the four edges by efw=ef(v)*W, where W denotes a weight function; and traversing the four edges, recording a pushable flow of each of the four edges, and pushing out a calculated flow. The ripple push method explores different push weight functions, and significantly improves the actual parallelism of the push-relabel algorithm.

Abstract: Provided are fusion proteins that include an apolipoprotein B mRNA editing enzyme catalytic subunit 3A (APOBEC3A) and a clustered regularly interspaced short palindromic repeats (CRISPR)-associated (Cas) protein, optionally further with uracil glycosylase inhibitor (UGI). Such a fusion protein is able to conduct base editing in DNA by deaminating cytosine to uracil, even when the cytosine is in a GpC context or is methylated.

Abstract: An image-based method of modeling and rendering a three-dimensional model of an object is provided. The method comprises: obtaining a three-dimensional point cloud at each frame of a synchronized, multi-view video of an object, wherein the video comprises a plurality of frames; extracting a feature descriptor for each point in the point cloud for the plurality of frames without storing the feature descriptor for each frame; producing a two-dimensional feature map for a target camera; and using an anti-aliased convolutional neural network to decode the feature map into an image and a foreground mask.

Abstract: A method of processing light field images for separating a transmitted layer from a reflection layer. The method comprises capturing a plurality of views at a plurality of viewpoints with different polarization angles; obtaining an initial disparity estimation for a first view using SIFT-flow, and warping the first view to a reference view; optimizing an objective function comprising a transmitted layer and a secondary layer using an Augmented Lagrange Multiplier (ALM) with Alternating Direction Minimizing (ADM) strategy; updating the disparity estimation for the first view; repeating the steps of optimizing the objective function and updating the disparity estimation until the change in the objective function between two consecutive iterations is below a threshold; and separating the transmitted layer and the secondary layer using the disparity estimation for the first view.

Abstract: The present disclosure provides an adaptive stereo matching optimization method, apparatus, and device, and a storage medium. The method includes: acquiring images of at least two perspectives of the same target scene, accordingly obtaining, through calculation, disparity value ranges corresponding to pixels in the target scene; and obtaining optimized depth value ranges by adjusting the disparity value ranges of the pixels in the target scene in real time through an adaptive stereo matching model; adjusting an execution cycle in the adaptive stereo matching model in real time through a DVFS algorithm according to a resource constraint condition of the processing system; and/or training on a plurality of scene image data sets through a convolutional neural network, so that the specific function parameters in the adaptive stereo matching model are correspondingly adjusted in real time according to the acquired different scene images.

Abstract: An enhanced dynamic random access memory (eDRAM)-based computing-in-memory (CIM) convolutional neural network (CNN) accelerator comprises four P2ARAM blocks, where each of the P2ARAM blocks includes a 5T1C ping-pong eDRAM bit cell array composed of 64×16 5T1C ping-pong eDRAM bit cells. In each of the P2ARAM blocks, 64×2 digital time converters convert a 4-bit activation value into different pulse widths from a row direction and input the pulse widths into the 5T1C ping-pong eDRAM bit cell array for calculation. A total of 16×2 convolution results are output in a column direction of the 5T1C ping-pong eDRAM bit cell array. The CNN accelerator uses the 5T1C ping-pong eDRAM bit cells to perform multi-bit storage and convolution in parallel. An S2M-ADC scheme is proposed to allot an area of an input sampling capacitor of an ABL to sign-numerical SAR ADC units of a C-DAC array without adding area overhead.

Abstract: A computer-implemented method includes encoding a radiance field of an object onto a machine learning model; conducting, based on a set of training images of the object, a training process on the machine learning model to obtain a trained machine learning model, wherein the training process includes a first training process using a plurality of first test sample points followed by a second training process using a plurality of second test sample points located within a threshold distance from a surface region of the object; obtaining target view parameters indicating a view direction of the object; obtaining a plurality of rays associated with a target image of the object; obtaining render sample points on the plurality of rays associated with the target image; and rendering, by inputting the render sample points to the trained machine learning model, colors associated with the pixels of the target image.

Abstract: According to some embodiments, an imaging processing method for extracting a plurality of planar surfaces from a depth map includes computing a depth change indication map (DCI) from a depth map in accordance with a smoothness threshold. The imaging processing method further includes recursively extracting a plurality of planar region from the depth map, wherein the size of each planar region is dynamically adjusted according to the DCI. The imaging processing method further includes clustering the extracted planar regions into a plurality of groups in accordance with a distance function; and growing each group to generate pixel-wise segmentation results and inlier points statistics simultaneously.

Abstract: A normal distributions transform (NDT) method for LiDAR point cloud localization in unmanned driving is provided. The method proposes a non-recursive, memory-efficient data structure occupation-aware-voxel-structure (OAVS), which speeds up each search operation. Compared with a tree-based structure, the proposed data structure OAVS is easy to parallelize and consumes only about 1/10 of memory. Based on the data structure OAVS, the method proposes a semantic-assisted OAVS-based (SEO)-NDT algorithm, which significantly reduces the number of search operations, redefines a parameter affecting the number of search operations, and removes a redundant search operation. In addition, the method proposes a streaming field-programmable gate array (FPGA) accelerator architecture, which further improves the real-time and energy-saving performance of the SEO-NDT algorithm. The method meets the real-time and high-precision requirements of smart vehicles for three-dimensional (3D) lidar localization.

Abstract: Provided are fusion proteins and related molecules useful for conducting base editing with reduced or no off-target mutations. The fusion protein may include a first fragment comprising a nucleobase deaminase or a catalytic domain thereof, a second fragment comprising a nucleobase deaminase inhibitor, and a protease cleavage site between the first fragment and the second fragment. Also provided are improved prime editing systems, including prime editing guide RNA with improved stability.

Abstract: Systems, methods, and non-transitory computer-readable media are configured to obtain a set of content items to train a neural radiance field-based (NeRF-based) machine learning model for object recognition. Depth maps of objects depicted in the set of content items can be determined. A first set of training data comprising reconstructed content items depicting only the objects can be generated based on the depth maps. A second set of training data comprising one or more optimal training paths associated with the set of content items can be generated based on the depth maps. The one or more optimal training paths are generated based at least in part on a dissimilarity matrix associated with the set of content items. The NeRF-based machine learning model can be trained based on the first set of training data and the second set of training data.

Abstract: A fusion protein which may comprise a first nCas9 fragment, a chimeric insertion fragment, a second nCas9 fragment and two UGI fragments from N-terminus to C-terminus, wherein the chimeric insertion fragment is selected from APOBEC1 fragment or APOBEC3A fragment for cytosine deamination at the target site. The fusion protein may comprise a first nCas9 fragment, a chimeric insertion fragment and a second nCas9 fragment from N-terminus to C-terminus, wherein the chimeric insertion fragment is TadA-TadA* for cytosine deamination at the target site. The present disclosure provides a novel base editing tool that is compatible with insertion of various deaminases on the chimeric sites of nCas9. Compared with nCas9 terminal fusion base editor, the base editing tool of the present invention significantly reduce off-targeting on both DNA and RNA, while maintaining specific targeted base editing efficiency, with higher specificity and favorable industrialization prospects.