4 results about "Outer product" patented technology

This invention proposes a robotic arm obstacle avoidance path planning method based on an improved ant colony algorithm, relating to the fields of robot path planning and intelligent optimization algorithms. This method endows the joint configuration space with a non-uniform metric structure induced by a metric tensor G(q), where G(q) is derived from the normalized joint inertia matrix W. I The algorithm consists of three parts: the singularity gradient outer product term and the obstacle spacing gradient outer product term. Local geodesic distances are approximated using the mean of the metric tensors at both ends of the node. All edge weights are pre-calculated and cached during the PRM graph construction phase. The ant colony uses the reciprocal of the geodesic distance as a heuristic function, drives non-uniform pheromone evaporation using the normalized value of the metric tensor trace increment, and uses the weighted sum of geodesic length cost and inertia-weighted velocity mutation penalty as the comprehensive path cost. The beetle whisker algorithm performs pre-search and completes non-uniform pheromone initialization under geodesic metrics. This method effectively improves path safety, continuity, and dynamic adaptability.

The embodiment of the invention belongs to the technical field of computers, and particularly relates to a polynomial calculation method and device and computer equipment. In the method, a first polynomial can be split into a product of a reference polynomial and a compensation exponential power, and outer product operation is performed on X first vectors corresponding to exponential powers of N input data and a second vector formed by coefficients of monomials of the same index in the reference polynomial through a matrix operation unit, and the matrix obtained by the outer product operation is accumulated to obtain the calculation result of the reference polynomial of the first polynomial corresponding to the plurality of input data, and then the calculation result is multiplied by the corresponding compensation exponent power to obtain the calculation result of the first polynomial. By the adoption of the method and device, part of the calculation process of the polynomial can be converted into matrix operation, implementation is achieved through the matrix operation unit, and the calculation efficiency of the polynomial can be improved.

The invention relates to a lightweight efficient parameter fine tuning processing method for a large model, which belongs to the technical field of natural language processing, and comprises the following steps of: converting information to be processed into a vector form through a coding layer of a pre-training model to obtain an input vector; introducing a dimension reduction matrix and a dimension raising matrix beside a query matrix and a value matrix of the pre-training model, wherein the dimension reduction matrix and the dimension raising matrix are both generated by outer accumulation of one-dimensional vectors; multiplying the input vector by the dimension reduction matrix and the dimension raising matrix in sequence to obtain a first feature vector; multiplying the input vector by an original weight matrix which is kept frozen in the pre-training model to obtain a second feature vector; according to the method, only the four groups of one-dimensional vector parameters for generating the dimension reduction matrix and the dimension raising matrix are finely adjusted, so that the problems of high calculation cost and difficulty in deployment in a resource-limited scene due to the fact that a mode of adjusting all parameters of a pre-training model is adopted in traditional full parameter fine adjustment are solved.

To reduce the number of times of access to an RF per product-sum operation.SOLUTION: For an L * N matrix A, an N * M matrix B, an L * M matrix C, and Cin, where L and M are both integers greater than or equal to 2 and N is an integer greater than or equal to 1, accumulating an outer product Ok of a k-th column of A and a k-th row of B in an array of L * M accumulators for an integer k greater than or equal to 0 and less than N; An array of l * m product-sum operators that perform an L * M * N matrix product operation C = A * B or an L * M * N matrix product-sum operation C = A * B + Cin, where either l or m is l = L or m = M, and the other is an integer of 2 ≤ l <L or 2 ≤ m <M, and the l * m product-sum operators perform accumulation of an outer product Ok in a plurality of steps.SELECTED DRAWING: Figure 17