Xilinx, Inc.

Kumar S S Vemuri

Aman Gayasen

Srijan Tiwary

Abid Karumannil

Vishal K Jain

Venkataraju Koppada

Sairam K M Menon

Anil K Martha

Venkat Praveen K Kancharlapalli

Anitha Barri

Pavan K Nadimpalli

Anusha Perla

Mahesh S Mahadurkar

Examples herein describe hardware architecture for processing and accelerating data passing through layers of a neural network. In one embodiment, a reconfigurable integrated circuit (IC) for use with a neural network includes a digital processing engine (DPE) array, each DPE having a plurality of neural network units (NNUs). Each DPE generates different output data based on the currently processing layer of the neural network, with the NNUs parallel processing different input data sets. The reconfigurable IC also includes a plurality of ping-pong buffers designed to alternate storing and processing data for the layers of the neural network.

Hardware architecture for a neural network accelerator

Examples herein describe a method for a compiler and hardware-abstraction-layer architecture for a programmable integrated circuit (IC). In one embodiment, a method for mapping and porting a neural network to an integrated circuit (IC) is disclosed. The method includes receiving a network description of the neural network; generating a framework independent network graph based on the network description; performing a plurality of back-end operations on the network graph to generate an execution sequence vector; and configuring the IC based on the execution sequence vector.

Compiler and hardware abstraction layer architecture for a neural network accelerator

Approaches for folding multiply-and-accumulate (MAC) logic in a circuit design involve a design tool recognizing a first instance of the MAC logic and a second instance of the MAC logic. The design tool replaces the first instance of the MAC logic and the second instance of the MAC logic with one instance of pipelined MAC logic. The design tool configures the pipelined MAC logic to input data signals of the first instance of the MAC logic and the second instance of the MAC logic to the pipelined MAC logic at a first clock rate, and switch between selection of the data signals of the first instance of the MAC logic and the second instance of the MAC logic at a second clock rate that is double the first clock rate. The design tool further configures the pipelined MAC logic to pipeline input data signals at the second clock rate, and to capture intermediate results at the second clock rate. The design tool further configures a register to capture output of the pipelined MAC logic at the first clock rate.

Folding multiply-and-accumulate logic

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Hyderabad Telangana, India

Kumar S S Vemuri