147 results about "Graph mapping" patented technology

The invention relates to the field of electric power monitoring, and discloses an electric power monitoring system and method fusing image recognition and data analysis, and the method comprises the steps: carrying out the visual angle coverage modeling of a target equipment group through a multi-type visual collection unit disposed at a transformer substation and a power distribution terminal; performing cross-frame fine-grained texture differential analysis on the equipment state image sequence, and constructing an image event time window in combination with synchronous disturbance characteristics of multi-source monitoring parameters; based on the high-vigilance candidate frame set, fusing the image structure variability index and the operation data multi-dimensional deviation vector by using a feature encoder, and constructing a multi-modal state coupling feature tensor; map mapping is carried out on the potential fault evolution trend, and semantic association is established between structural nodes with abnormal attributes in the image and frequently fluctuating parameter indexes in the monitoring data; and combining a node interference path in the local fault association subgraph with fault precursor distribution induced in a historical accident sample. The method has the advantage that the operation safety is improved.

The invention discloses a multi-label electrocardiogram classification method based on self-supervised pre-training and multi-modal semantic alignment, which belongs to the technical field of artificial intelligence, and comprises the following steps: realizing self-supervised pre-training of unlabeled data through a single-modal contrast enhancement network, generating global and local contrast views by adopting a multi-scale random cutting strategy, and classifying the global and local contrast views in a multi-scale random cutting mode; in combination with a teacher-student network architecture, the potential invariance features of the ECG signals are learned while negative sample dependence is avoided, the problem of annotation data scarcity is effectively relieved, and the feature robustness is improved. A multi-modal fusion mechanism based on label semantic guidance is provided, a time domain signal and a frequency domain time-frequency graph are mapped to a unified semantic space through fine-grained semantic alignment, local feature enhancement and cross-modal complementary information fusion are realized by using a cross attention mechanism, and the problem of semantic difference caused by modal heterogeneity in a traditional method is overcome. A multi-label comparison loss function based on a disease co-occurrence relation is proposed, a category discrimination boundary is dynamically optimized by modeling a label co-occurrence probability, the feature separability of a tail category is improved while the head category discrimination ability is enhanced, and the problem of sample category imbalance in a multi-label scene is remarkably relieved.

The invention relates to a data query analysis method based on a natural language, which comprises the following steps of: analyzing a natural language query request input by a user into a dynamic semantic intention tensor which comprises an operation type, a comparison relationship, a time constraint and a data role semantic dimension, and dynamically combining according to task semantics to form a complete data query and analysis operation expression; mapping the natural language intention to a controllable data operation space of a database, and generating a preliminary data operation chain; performing side effect perception processing on the operation tensor, predicting a potential execution consequence of the operation, generating a side effect tensor for simulating a side effect of the database operation, and if any index in the side effect tensor exceeds a preset threshold value, performing local correction on the operation tensor; according to the method, a complex natural language query task is disassembled into a plurality of minimum semantic units by adopting a step-by-step semantic resolution mechanism, each unit is mapped into a specific tensor operation, and a dependency relationship between operation units is dynamically reasoned to generate an operation execution chain.

Techniques for computer science, data science, data analytics, computer software, algorithmic analysis, and networked technologies for sourcing, procurement, manufacturing, and supply chain management in small-to-medium manufacturing (“SMM”) industries that involve processes whereby implementing user input and extracted image and text from engineering drawings may be used to map manufacturing requirements to a subset of manufacturers via natural language agent programs. More specifically, natural language agent programs may be implemented to apply input and extracted image and text from engineering drawings to a large language model (“LLM”). An example method may include receiving a user input including data representing requirements to manufacture a physical structure, concatenating an engineering drawing summary data, a shoptype description, and estimated part size instruction data to combine the at least two of the engineering drawing summary data, the shoptype description, and the estimated part size instruction data to generate a subset (e.g., a list) of qualified manufacturers (e.g., SMMs).

The invention discloses a large-scale heterogeneous computing power cluster training and pushing acceleration method, which comprises the following steps: S1, collecting operation characteristics of each node of a heterogeneous cluster, and constructing an equipment state parameter set; s2, loading a training and reasoning model, constructing an intermediate representation graph structure, and generating graph structure information; s3, inputting the graph structure information and the equipment state parameter set into a scheduling strategy model constructed based on a neural network ordinary differential equation, and generating an optimization decision result; s4, reconstructing a model graph according to an optimization decision result, and dividing the model graph into a training sub-graph and a reasoning sub-graph; s5, mapping the sub-graphs to heterogeneous nodes, performing resource matching and task distribution according to the equipment state parameter set, and generating a scheduling result; s6, collecting performance feedback information; and S7, inputting feedback information into the scheduling model to update parameters, and adjusting evolution function strategy parameters. According to the method, the scheduling acceleration of the training and pushing task of the deep model in the heterogeneous cluster is realized.

The invention relates to a distributed source-load collaborative optimization method based on high-order topology and multi-scale attention, and the method comprises the steps: firstly providing a high-order graph construction method driven by structural interaction, and achieving the structural embedded expression of a physical interaction relation between multi-source equipment through a hyperedge-line graph mapping mechanism and functional attribute coding; secondly, a graph feature extraction method based on a multi-scale joint attention mechanism is designed, topology and state information are fused, and the inter-node adjustment collaboration recognition capability is improved; further constructing a source-load collaborative optimization scheduling model, introducing a particle swarm optimization algorithm to obtain an initial feasible strategy, and establishing a state-action mapping relation based on a deep reinforcement learning framework driven by graph embedding to realize autonomous learning and rolling optimization of a distributed control strategy; and finally, constructing an operation feedback closed loop mechanism, and introducing a graph structure migration and strategy adaptive updating method to enhance the response capability of the system to topological change and dynamic disturbance.

The invention relates to the technical field of cognitive inference, and discloses a cognitive inference system fusing a dynamic knowledge graph and deep learning. The system comprises a cognitive event stream processing module which is used for acquiring a real-time event stream and a cognitive target stream, identifying entity state change and extracting a reasoning intention; the graph mapping generation module is used for receiving the entity state change and the reasoning intention, constructing a mapping relation with knowledge graph topology and generating a multi-dimensional graph mapping library; the cognitive path decision-making module is used for extracting key topological characteristics from the multi-dimensional map mapping library, calculating a reasoning path migration probability and generating an initial cognitive path set; the time sequence evolution prediction module is used for carrying out time sequence evolution modeling on the knowledge graph topology based on the initial cognitive path set, identifying node state offset and updating a multi-dimensional graph mapping library; and the reasoning optimization module is used for analyzing a reasoning stage and a reasoning dependency relationship of the cognitive target flow according to the updated multi-dimensional map mapping library, and generating a dynamic cognitive reasoning library.

The invention discloses a graph mapping method based on precise synthesis, and the method comprises the steps: constructing a precise synthesis structure library based on NPN equivalence classes, and inputting the precise synthesis structure library into a Boolean network; k-Cut cutting enumeration is carried out on the input Boolean network, a truth table is calculated, cutting is matched with a structure in a precise comprehensive structure library through Boolean matching, and rapid search is realized in combination with NPN classification; the overlay network is generated through multi-round mapping optimization, logic sharing nodes are mined in combination with structural hash, and the multi-round mapping optimization comprises delay-oriented mapping, global area topological optimization and local accurate area optimization; and a new target network is generated based on the optimized overlay network, redundant nodes are removed, and final network construction is completed. According to the method, manual intervention is not needed through full-process automatic mapping and a redundancy removal mechanism, the labor cost of a technical mapping link in chip design is remarkably reduced, and the method is particularly suitable for efficient design of complex circuits and emerging majority of logic base technologies.

An apparatus and method for improving ray tracing efficiency. For example, one embodiment of an apparatus comprises: An apparatus comprising: a binary instrumentation engine to perform binary instrumentation of ray tracing shaders and to trace execution of the ray tracing shaders to generate execution metrics; call graph construction logic to construct a shader call graph based on the execution metrics; shader source mapping logic to map the shader call graph to shader source code to generate a source code map; efficiency analysis logic to determine inefficiencies in ray tracing shader execution based on the source code map; and optimization logic to identify optimization actions based on the inefficiencies.

The invention discloses a space transcriptome data analysis method based on edge weighted graph attention and multi-view comparative learning, which comprises the following steps: preprocessing space transcriptome data to obtain preprocessed space transcriptome data; wherein the spatial transcriptome data comprises histological information, gene expression information and spatial position information; constructing a cell adjacency matrix, a disturbance matrix and a gene co-expression matrix based on the preprocessed space transcriptome data; based on the cell adjacency matrix, the perturbation matrix and the gene co-expression matrix, a multi-view comparison learning framework is constructed, and multiple views comprise a front view, a space view and a negative view; the multiple views are mapped to a unified potential representation space through an edge weighted graph attention auto-encoder, and node embedding is obtained; downstream analysis is performed based on node embedding. According to the method, different space structure modes can be accurately identified in space transcriptome data analysis, so that the clustering accuracy, compactness and separability of space data are improved.

The invention relates to the technical field of path planning, in particular to a vehicle bottom detection robot local path planning method based on cross-modal collaborative learning. The method comprises the following steps: carrying out data preprocessing on acquired multi-modal sensing data; according to the preprocessed data and based on a cross-modal collaborative modeling mechanism, constructing a multi-modal fusion perception feature map; mapping the multi-modal fusion perception feature map into a local path navigation map based on path planning; selecting an optimal path in the local path navigation map based on a path generation mechanism; and performing optimal path optimization based on a state error estimation mechanism. And the functions of deep fusion of multi-modal data, dynamic mapping of spatial geometry and semantic categories, modal confidence self-adjustment, execution feedback correction and the like are realized. Experimental verification shows that the success rate of the path planning task can be increased to 95.3% from 78.6% in a typical vehicle bottom complex scene.

According to the maculopathy diagnosis device and method based on GCN and Mama cooperation and the medium, feature interaction processing is carried out by designing a GMMN module (a graph mapping Mama network), each pixel is regarded as a feature node, and through combination of graph convolution and Mama coding, the advantages of the two methods are brought into full play, and the respective limitations of the two methods are overcome at the same time. Furthermore, a mapping module is developed, and effective cross-branch feature fusion is realized by using a geometrical relationship between feature nodes. A large number of experimental results prove that the network has excellent performance when the CFP modal image is used for automatic diagnosis of macular edema, and the effectiveness and clinical application potential of the method are verified. According to experimental verification on two public data sets of IDRID and Messidor and one private data set, 91.26%, 94.58% and 98.43% of diagnosis accuracy are achieved respectively, it is indicated that the method can achieve a good diagnosis effect on different data sets, and the huge potential of the method in clinical application is highlighted.

The invention discloses an artificial intelligence-based refrigeration station energy-saving control method and system, and relates to the technical field of energy-saving control, and the method comprises the steps: collecting multi-modal data, carrying out the optimization through a PINN neural network, generating a global thermal field graph and an airflow field through a QVC circuit and a quantum variation optimization algorithm, mapping the global thermal field graph to a quantum Hilbert space, and carrying out the optimization of the global thermal field graph and the airflow field. The method comprises the following steps: predicting a cold load demand through NQ-GRU, generating a chaos sequence by using Sine chaos mapping, extracting local thermal field characteristics, generating actions of equipment parameters through DQN, disturbing actions of optimal equipment parameters by using the chaos sequence, converting into a PLC instruction, and generating an equipment operation instruction set. According to the invention, optimization is carried out by combining an OFT circuit with a PINN neural network, a quantum variational optimization algorithm is introduced, the precision and efficiency of energy-saving control of the refrigeration station are improved, the output of a model is disturbed through a chaotic mapping technology, and the flexibility of equipment operation and the adaptive ability of the system are improved.

The invention provides a user behavior intention recognition method and device based on multi-modal data fusion, and relates to the technical field of data processing.The method comprises the steps that a multi-modal observation sequence is obtained, initial features are formed through time synchronization and mutual information screening, a cross-space initial offset vector is generated in combination with virtual space offset parameters, and the cross-space initial offset vector is obtained; establishing an association relationship between a real action and a virtual action through nonlinear mapping to obtain a cross-space registration feature; generating a correction coefficient according to the proportion of the real action amplitude and the virtual action amplitude, and performing amplitude correction to obtain a cross-space correction feature sequence; compensating rendering delay and synchronous drift by using weighted fusion of the current feature and the lag feature to obtain a time sequence correction feature; outputting cross-space fusion features through a unified embedding space and a cross-modal attention mechanism; and non-linear intention mapping is constructed based on the fusion features, and a user behavior intention result is inferred and output in combination with time smoothing. According to the invention, the accuracy of user behavior intention analysis in a virtual field scene can be improved.

The invention discloses an interactive document editing canvas method, which comprises the following steps of: establishing a data-view mapping relation directly driven by a Vue2 responsive system between a canvas container and an element array, any addition, deletion and modification operation aiming at the node description data set can be instantly and accurately reflected to the style and attribute of the corresponding DOM child node without manual intervention, so that element positioning, rendering and follow-up editing are completed in the same technical action, the delay and inconsistency risk caused by frequent and direct operation of a real DOM in a traditional scheme is eliminated, and the operation efficiency is improved. The interaction fluency, the layout intuition and the system maintainability are obviously improved; by means of a virtual DOM mechanism of Vue2, batch diff is carried out and then submitted to a real DOM in a unified mode, the number of rearrangement and redrawing times of a browser is reduced, CPU and memory occupation is further reduced, and the canvas still keeps smooth response even in the scene with dense elements and frequent updating.

The invention relates to the technical field of constructional engineering information retrieval, and discloses a constructional engineering business opportunity information retrieval agent system and method based on a large model. The system comprises an information acquisition module, an intention mapping module, a mapping relation construction module, a business opportunity matching module, a dynamic updating module and a retrieval management module. The information acquisition module acquires multi-modal business opportunity information from a multi-heterogeneous data source, and generates a business opportunity feature warehouse through integration and feature extraction; the intention mapping module receives a user retrieval request, analyzes a semantic intention by means of a big language model to generate an intention feature vector, and performs preliminary matching in a business opportunity feature warehouse; the mapping relation construction module constructs a dynamic mapping relation between intention and business opportunity feature space according to the association strength; the business opportunity matching module combines the relationship with the time weight to calculate semantic relevancy, and generates a business opportunity matching sequence; the dynamic updating module monitors data change, updates the warehouse and adjusts the mapping relation; the retrieval management module generates a hierarchical retrieval strategy, constructs a business opportunity retrieval knowledge base, and meets the retrieval requirements of building enterprises.

A virtual assistant platform implemented by a computer system comprising: one or more hardware processors configured to execute computer readable instructions; one or more memory storing the instructions; a mapping data structure stored in the one or more memory, the mapping data structure mapping a plurality of intents to respective client specific actions; a network interface configured to receive a query from a user device operating in a client specific communication session with a virtual assistant in a first context, the instructions when executed providing: an AI language model comprising a client specific language model, the client specific language model having been trained on client specific data, and a mesh language model, the mesh language model having been trained on mesh specific data, the mesh specific data having been received by operating multiple virtual assistants in the first context, the AI language model being responsive to the query to generate an intent; a mapping function to apply the intent to the mapping data structure and access a corresponding client specific action for delivery of a response to the user device; and a transmission function to transmit the response to the user device.

The present application provides a software development resource scheduling method and system based on big data. Among them, the method includes: mapping the heterogeneous configuration feature graph to the hyperbolic embedding space, combining the curvature adaptive algorithm, measuring the environmental configuration similarity value between the client server and the existing business servers in the pre-built resource pool, and the resource pool is constructed based on historical big data; according to the resource type required for the software development task provided by the client server, screening the existing business servers with configuration similarity values ​​greater than the preset similarity threshold and matching resource types from the resource pool; sorting the called business servers in descending order according to the similarity value, and elastically scheduling the software development resources in the called business servers based on the sorting results. The present application utilizes a mechanism for dynamically evaluating the environmental configuration similarity value, and combines it with a priority scheduling strategy to achieve zero-dependency conflict, high-precision, and highly effective resource scheduling, as well as the elastic reuse capability of resources.

A construction knowledge base comprises structured construction data, arranged in a knowledge graph (G) with entities and relations between the entities. A user query (X) is mapped to a set of entity relations ({circumflex over (R)}) using a first generator model (Md), trained to map unstructured construction data to structured entity relations. A subgraph (Z) is retrieved from the knowledge graph (G), using the set of entity relations for the query ({circumflex over (R)}). The subgraph (Z) is mapped to unstructured data (Y) as output for the query, using a second generator model (Mg), inverse to the first generator model (Md) and trained to map structured entity relations to unstructured construction data. The generator models (Mg, (Md)) are trained for cycle consistency, whereby structured entity relations output by the first generator model (Md) are input to the second generator model (Mg), and unstructured data output by the second generator model (Mg) is input to the first generator model (Md).

The invention provides a three-dimensional part retrieval method based on graph similarity search, relates to the field of computer graphics, and solves the technical problems that in the prior art, geometric and design semantic information of CAD cannot be fully utilized, and a topological relation is difficult to capture and display, so that the retrieval efficiency is low. The method comprises the following steps: acquiring part three-dimensional data of a computer-aided design (CAD) model, and constructing a training data set; constructing an edge-surface connection diagram based on the three-dimensional data of the part; calculating a graph editing distance (GED) matrix of all edge surface connection graphs in the training data set as a supervision signal; based on the GED matrix, training a sorting model, mapping an edge-surface connection graph to a hidden space, and constructing a part vector database according to an output graph-level embedding vector; the sorting model is constructed based on a graph attention network; and inputting a to-be-queried CAD part into the trained sorting model to obtain a feature vector, carrying out nearest neighbor search in the part vector database, and returning a similar part result.

The invention relates to the technical field of artificial intelligence, in particular to an intelligent customer service dialogue generation system and method based on a knowledge graph, and the system comprises a language model, a knowledge graph module, a query module, a matching module, a reasoning module, a strategy generator, a reinforcement learning unit and a language generation module. The module comprises a knowledge subgraph isomorphic mapping unit, a graph entropy gradient reasoning unit and a Markov decision optimization unit, and the three units form a progressive processing link. The knowledge sub-graph isomorphic mapping unit maps the retrieval sub-graph to an isomorphic sub-graph in a knowledge graph topological space; the map entropy gradient reasoning unit constructs an entropy gradient field to realize reasoning path optimization from a high-entropy region to a low-entropy region; the Markov decision optimization unit models an inference process into a Markov decision process, a reinforcement learning method optimizes an inference strategy, and effective inference from dominant knowledge to implicit knowledge is realized in combination with a knowledge graph and a deep reinforcement learning technology.

There is provided a computer implemented method of managing access for user account, comprising: accessing a graph mapping a plurality of nodes denoting real users to a plurality of user accounts and a plurality of resources hosted by a plurality of service computing environments external to a target computing environment accessed by the user accounts, and defining different permissions for user accounts for accessing the different resources, receiving a query for identifying user accounts for a target user, executing the query on the graph, and providing details regarding user accounts of the target users from the execution of the query on the graph, the details including at least one of: identifiers of the user accounts, resources accessible to the user accounts, access privileges for accessing the resources.

The invention relates to the technical field of enterprise information collection, in particular to an intelligent collection and evaluation method and system for enterprise dynamic operation data, and the method comprises the steps: carrying out the homomorphic encryption and evidence fixation of transaction, energy consumption and positioning data through a zero-trust probe; optical reserve calculation expands energy consumption characteristics, generates an acyclic causal graph with transaction and positioning information through topological coherence and quantum annealing, and converts the acyclic causal graph into a Shenchang differential equation to obtain causal embedding; macroscopic disturbance is generated through quantum kicking, management action is generated through conditional diffusion, a dynamic equation is driven to deduce an anti-fact trajectory, and a structure risk index is calculated; and when the index exceeds the threshold, mapping the abnormal sub-graph to a membrane memory array solution average field game, obtaining an adaptive strategy write-back model through federal distillation, and synchronously consolidating. According to the invention, millisecond early warning and microsecond intervention are realized, and extreme risks are covered with low energy consumption.

The invention relates to the technical field of biological detection signal analysis, and discloses a method for rapidly detecting the content of active collagen. The method comprises the following steps: collecting an original optical response signal of a reaction between a to-be-detected solution and a specific reagent, and constructing a time-space continuous primary reaction track through multi-level slicing and cross-domain splicing of a time domain, a frequency domain and a time-frequency domain; a signal mode set related to activity is separated through convolution kernel group depth scanning. A graph association network between signal modes is constructed based on the set, and a steady-state feature graph representing reaction kinetics is generated through iterative calculation. The feature map is mapped to a virtual reaction space defined by standard data, and quantitative judgment is achieved by calculating the distance between a projection vector and a preset concentration anchor point. According to the method, the sensitivity and the anti-interference performance of detection are enhanced through deep analysis reaction dynamic signals and correlation network modeling.

The invention discloses a big data relation mining analysis method based on a graph neural network, which comprises the following steps: S1, acquiring multi-source data, extracting data features, constructing a data relation graph, and mapping the data relation graph into a low-dimensional vector as a basic data structure; s2, inputting a low-dimensional vector through the graph neural network model, outputting a prediction result, calculating an error by using a loss function based on a real relation label, adjusting parameters of the graph neural network model, and obtaining a target data model; s3, obtaining a data relationship prediction result through the target data model, mining a data potential relationship, and mining a data relationship result through the data potential relationship; and S4, combining a data relationship result with domain knowledge and business rules, constructing a relationship knowledge base, and displaying the data relationship through a visualization technology. According to the method, efficient and accurate big data relationship mining is realized, and the accuracy and practicability of potential relationship discovery in a complex data environment are improved.

The invention discloses a task processing method and device based on quantum computing, equipment and a storage medium. The method comprises the steps of determining a maximum cut problem corresponding to a target task and a weighted undirected graph of the maximum cut problem; performing community detection division on the weighted undirected graph to obtain a plurality of community sub-graphs; mapping the community sub-graph into a sub Hamiltonian and constructing a parameterized quantum circuit; updating parameters of the parameterized quantum circuit by adopting a gradient descent algorithm to minimize a global Hamiltonian expected value, and outputting a binary string of a quantum state corresponding to the global Hamiltonian expected value as an initial solution; generating a plurality of candidate solutions for the initial solution based on a neighborhood search algorithm, calculating cut values of the initial solution and the candidate solutions, and selecting a better feasible solution based on the optimal cut value; and applying a preset disturbance operator in the parameterized quantum circuit to construct a disturbance quantum circuit, updating parameters of the disturbance quantum circuit by adopting a gradient descent algorithm, and outputting a binary string of a quantum state corresponding to the global Hamiltonian expected value as a final solution of the target task by minimizing the global Hamiltonian expected value after disturbance.

A method includes receiving one or more input Artificial Intelligence (AI) networks, transforming the AI networks into respective graphs including interconnected logical operators, and mapping the graphs onto a design of a hardware accelerator including a plurality interconnected hardware engines. A performance of running the AI networks on the design of the hardware accelerator is simulated using a petri-net simulation.

This invention relates to the field of electronic digital data processing and digital information transmission technology, specifically to a cloud-based intelligent data processing system for multiple nodes. The system includes a data preprocessing unit, a topology-aware scheduling engine, a graph neural network device, and a task execution unit. The data preprocessing unit extracts the temporal dependencies and logical associations of the input data stream to generate a directed acyclic graph (DAG). The topology-aware scheduling engine maps the graph to the physical network topology and calculates link bandwidth margin and congestion status. The graph neural network device jointly encodes node computational load and edge link status to output a task allocation strategy. The task execution unit schedules strongly dependent tasks to adjacent nodes with sufficient link margin and idle computing power according to the strategy, and dynamically migrates tasks to alternative path nodes based on link packet loss rate during execution. This invention reduces data transmission latency across switches in multi-node cascading and avoids node computing power idleness caused by local link congestion.

The application belongs to the technical field of image segmentation, and particularly relates to a complex scene target segmentation method and system based on image recognition, which comprises the following steps: performing superpixel adaptive division on an input image, fusing gray scale and texture features to determine a superpixel boundary, mapping the superpixel boundary into a graph node and calculating an edge weight, and constructing an undirected weighted graph; adaptively encoding a graph signal, utilizing a hybrid graph wavelet-Fourier joint transform to optimize and separate features, and obtaining purified graph frequency domain features; sparsely reconstructing features through adaptive super-complete dictionary learning, and obtaining target enhanced features; extracting topological parameters based on an improved persistent homology, constructing a topological constraint feature graph, mapping the topological constraint feature graph into an initial contour field, iteratively optimizing a level set and a contour through an adaptive partial differential equation, and obtaining a high-fidelity coarse segmentation result; extracting geometric features to construct a joint constraint model to repair an occluded area, and outputting a precise segmentation result. In the application, sparse topological modeling is adopted, weak features are strengthened, and target discrimination accuracy is improved.

A computer-implemented method of transforming a high-level program for mapping onto a coarse-grained reconfigurable (CGR) processor with an array of CGR units, including sectioning a dataflow graph into a plurality of sections; extracting performance information for each of the plurality of sections; on a CGR unit: assigning to a section at least two computations dependent on a first data element; scheduling an additional load of the first data element in response to available memory bandwidth for that section; eliminating a buffer between the additional load of the first data element and one of the two computations, for that section; generating configuration data for the placed positions and the routed data and communication channels, wherein the configuration data, when loaded onto an instance of the array of CGR units, causes the array of CGR units to implement the dataflow graph; and storing the configuration data in a non-transitory computer-readable storage medium.