Abstract: A hardware-aware zero-cost neural network architecture search system is configured to perform the following. A neural network search space is divided into multiple search blocks. Each of the search blocks includes multiple candidate blocks. The candidate blocks are guided and scored through a latent pattern generator. The candidate blocks in each of the search blocks are scored through a zero-cost accuracy proxy. One of the candidate blocks included in each of the search blocks is sequentially selected as selected candidate blocks, the selected candidate blocks are combined into multiple neural networks to be evaluated, and network potential of the neural networks to be evaluated is calculated according to scores of the selected candidate blocks. One neural network to be evaluated with the highest network potential is selected to determine the corresponding selected candidate blocks.

Abstract: A method, a baseboard management controller (BMC) and a system for establishing a serial-over-LAN connection are provided. The method includes: receiving data from a server unit using the first UART at a current baud rate; detecting the current baud rate of the first UART using the BMC; configuring the baud rate of the second UART of the BMC as the current baud rate; controlling the second UART to retrieve the data from the first UART at the current baud rate using the BMC; and sending the data to a network using the BMC.

Abstract: A method, a baseboard management controller (BMC) and a system for establishing a serial-over-LAN connection are provided. The method includes: receiving data from a server unit using the first UART at a current baud rate; detecting the current baud rate of the first UART using the BMC; configuring the baud rate of the second UART of the BMC as the current baud rate; controlling the second UART to retrieve the data from the first UART at the current baud rate using the BMC; and sending the data to a network using the BMC.