Abstract: A radio-frequency power amplification module for a magnetic resonance system and an imaging method are provided. The radio-frequency power amplification module includes: a power synthesizer, a main amplifier, and an auxiliary amplifier, output ends of the main amplifier and the auxiliary amplifier being both connected to a power synthesis unit; and a controller. The controller is configured to output a control signal according to a required radio-frequency transmission parameter or a scan parameter corresponding to the radio-frequency transmission parameter, so as to adjust a control parameter of the auxiliary amplifier. The radio-frequency power amplification module has high efficiency.

Abstract: Provided in an embodiment of the present application are a magnetic resonance imaging system and a transmission apparatus and a transmission method. The method comprises: generating and outputting a pulse signal by a transmission controller; amplifying the pulse signal by a radio-frequency amplifier; transmitting, by a signal processor, the signal amplified by the radio-frequency amplifier to a transmit coil of the magnetic resonance imaging system; and generating frequency offset lookup information for bandwidth compensation of the entire transmission apparatus according to bandwidth data of the transmission controller, the radio-frequency amplifier, and the signal processor; wherein the frequency offset lookup information is used to control the transmission controller to generate output power.

Abstract: The present technical disclosure relates to artificial neural networks, e.g., gated recurrent unit (GRU). In particular, the present technical disclosure relates to how to implement a hardware accelerator for compressed GRU based on an embedded FPGA. Specifically, it proposes an overall design processing method of matrix decoding, matrix-vector multiplication, vector accumulation and activation function. In another aspect, the present technical disclosure proposes an overall hardware design to implement and accelerate the above process.

Abstract: Hardware accelerator for compressed Long Short Term Memory (LSTM) is disclosed. The accelerator comprise a sparse matrix-vector multiplication module for performing multiplication operation between all sparse matrices in the LSTM and vectors to sequentially obtain a plurality of sparse matrix-vector multiplication results. A addition tree module are also included for accumulating a plurality of said sparse matrix multiplication results to obtain an accumulated result. And a non-linear operation module passes the accumulated results through an activation function to generate non-linear operation result. That is, the present accelerator adopts pipeline design to overlap the time of data transfer and computation for compressed LSTM.

Abstract: Hardware accelerator for compressed Long Short Term Memory (LSTM) is disclosed. The accelerator comprise a sparse matrix-vector multiplication module for performing multiplication operation between all sparse matrices in the LSTM and vectors to sequentially obtain a plurality of sparse matrix-vector multiplication results. A addition tree module are also included for accumulating a plurality of said sparse matrix multiplication results to obtain an accumulated result. And a non-linear operation module passes the accumulated results through an activation function to generate non-linear operation result. That is, the present accelerator adopts pipeline design to overlap the time of data transfer and computation for compressed LSTM.

Abstract: An apparatus for achieving an accelerator of a sparse convolutional neural network is provided. The apparatus comprises a convolution and pooling unit, a full connection unit and a control unit. Convolution parameter information and input data and intermediate calculation data are read based on control information, and weight matrix position information of a full connection layer is also read. Then a convolution and pooling operation for a first iteration number of times is performed on the input data in accordance with the convolution parameter information, and then a full connection calculation for a second iteration number of times is performed in accordance with the weight matrix position information of the full connection layer. Each input data is divided into a plurality of sub-blocks, and the convolution and pooling unit and the full connection unit perform operations on the plurality of sub-blocks in parallel, respectively.

Abstract: The present technical disclosure relates to artificial neural networks, e.g., gated recurrent unit (GRU). In particular, the present technical disclosure relates to how to implement a hardware accelerator for compressed GRU based on an embedded FPGA. Specifically, it proposes an overall design processing method of matrix decoding, matrix-vector multiplication, vector accumulation and activation function. In another aspect, the present technical disclosure proposes an overall hardware design to implement and accelerate the above process.

Abstract: The present invention proposes a highly parallel solution for implementing ANN by sharing both weights matrix of ANN and input activation vectors. It significantly reduces the memory access operations, the on-chip buffers. In addition, the present invention considers how to achieve a load balance among a plurality of on-chip processing units being operated in parallel. It also considers a balance between the I/O bandwidth and calculation capabilities of the processing units.