Abstract: Memory allocation methods and IC products utilizing the same. A memory space with a continuous address is provided. A first memory address length is allocated to a first pin according to a data amount obtained through the first pin. A second memory address length allocated adjacent to the first memory address length is allocated to a second pin according to a data amount obtained through the second pin.

Abstract: A data processing method based on a serial transmission interface applied to PCI Express data transmission. A data packet is received by a physical layer. The data packet is transmitted to a data link layer and a transaction layer simultaneously. The data packet is processed by the data link layer and the transaction layer simultaneously.

Abstract: A method for changing the CPU frequency under control of a neural network. The neural network has m basis functions and n basis points that are connected together. Using the learning capability of the neural network to deduce basis weights based on dummy environmental parameters and a dummy output vector. In an application procedure, environmental parameters are input to the basis points and basis vectors are calculated based on the basis functions. Integrating the multiplication of each basis vector and its corresponding basis weight, an output vector can be generated to determine a control signal so that the CPU can be controlled to raise or lower its operating frequency. In addition, if the user has to change the parameters due to behavior, a fast learning function of a radial neural network can be used for complying with each user's behavior.

Abstract: Memory allocation methods and IC products utilizing the same. A memory space with a continuous address is provided. A first memory address length is allocated to a first pin according to a data amount obtained through the first pin. A second memory address length allocated adjacent to the first memory address length is allocated to a second pin according to a data amount obtained through the second pin.

Abstract: A method for changing the CPU frequency under control of a neural network. The neural network has m basis functions and n basis points that are connected together. Using the learning capability of the neural network to deduce basis weights based on dummy environmental parameters and a dummy output vector. In an application procedure, environmental parameters are input to the basis points and basis vectors are calculated based on the basis functions. Integrating the multiplication of each basis vector and its corresponding basis weight, an output vector can be generated to determine a control signal so that the CPU can be controlled to raise or lower its operating frequency. In addition, if the user has to change the parameters due to behavior, a fast learning function of a radial neural network can be used for complying with each user's behavior.