Xilinx, Inc.

Beijing Deephi Intelligent Technology Co., Ltd.

Beijing Guodian Futong Science and Technology Development Co., Ltd.

Beijing Nanopep Biotech Corporation, Ltd.

Qian Yu

Lei Zhang

Yu Han

Ning Zhang

Yi Shan

Ran Wang

Da Li

Lingzhi Sui

An Xu

Shaoxia Fang

Junbin Wang

Qian Cai

Yuwei Wang

Zhenqiang Liu

Nannan Li

Xuejiao Bai

Zhen Cai

Haiyan Qiu

Jian Xue

A high parallelism computing system and instruction scheduling method thereof are disclosed. The computing system comprises: an instruction reading and distribution module for reading a plurality of types of instructions in a specific order, and distributing the acquired instructions to corresponding function modules according to the types; an internal buffer for buffering data and instructions for performing computation; a plurality of function modules each of which sequentially executes instructions of the present type distributed by the instruction reading and distribution module and reads the data from the internal buffer; and wherein the specific order is obtained by topologically sorting the instructions according to a directed acyclic graph consisting of the types and dependency relationships. By reading the instructions based on the topological sorting the directed acyclic graph constructed according to the types and dependency relationships, the deadlock caused by the instruction dependencies can be avoided by a relatively simple operation.

High parallelism computing system and instruction scheduling method thereof

The present disclosure relates to a processor for implementing artificial neural networks, for example, convolutional neural networks. The processor includes a memory controller group, an on-chip bus and a processor core, wherein the processor core further includes a register map, an instruction module, a data transferring controller, a data writing scheduling unit, a buffer module, a convolution operation unit and a hybrid computation unit. The processor of the present disclosure may be used for implementing various neural networks with increased computation efficiency.

Device for implementing artificial neural network with separate computation units

The present disclosure relates to a processor for implementing artificial neural networks, for example, convolutional neural networks. The processor includes a memory controller group, an on-chip bus and a processor core, wherein the processor core further includes a register map, an instruction module, a data transferring controller, a data writing scheduling unit, a buffer pool, a data reading scheduling unit and a computation module. The processor of the present disclosure may be used for implementing various neural networks with increased computation efficiency.

Device for implementing artificial neural network with flexible buffer pool structure

The present disclosure relates to a processor for implementing artificial neural networks, for example, convolutional neural networks. The processor includes a memory controller group, an on-chip bus and a processor core, wherein the processor core further includes a register map, a first instruction unit, a second instruction unit, an instruction distributing unit, a data transferring controller, a buffer module and a computation module. The processor of the present disclosure may be used for implementing various neural networks with increased computation efficiency.

Device for implementing artificial neural network with multiple instruction units

A bottom ash discharging device for a coal-fired boiler including: a clinker case, installed between a bottom ash hopper outlet of the coal-fired boiler and a bottom ash conveyer, an impact bar including a plurality of heat-resistant metal rods supporting inside the clinker case, a clinker crusher, installed above the impact bar for crushing the clinker, and a guiding device, installed between the bottom ash hopper outlet and the clinker crusher for limiting the clinker to drop into the clinker crushing area of the clinker crusher. The bottom ash discharging device ensures that the clinkers drop from the bottom ash hopper outlet to the rear area of the clinker crusher, and that the impact bar can be replaced and repaired without stopping the coal-fired boiler.

Bottom ash discharging device for coal-fired boiler

Growing community of inventors

Beijing, China

Qian Yu