View source for US Patent Application 18218426. SEMI-PROGRAMMABLE AND RECONFIGURABLE CO-ACCELERATOR FOR A DEEP NEURAL NETWORK WITH NORMALIZATION OR NON-LINEARITY simplified abstract

=SEMI-PROGRAMMABLE AND RECONFIGURABLE CO-ACCELERATOR FOR A DEEP NEURAL NETWORK WITH NORMALIZATION OR NON-LINEARITY=


==Organization Name==

[[Microsoft Technology Licensing, LLC]]


==Inventor(s)==

[[Stephen Sangho Youn of Bellevue WA (US)]]


[[Steven Karl Reinhardt of Vancouver WA (US)]]


[[Jeremy Halden Fowers of Seattle WA (US)]]


[[Lok Chand Koppaka of Bellevue WA (US)]]


[[Kalin Ovtcharov of Snoqualmie WA (US)]]


==SEMI-PROGRAMMABLE AND RECONFIGURABLE CO-ACCELERATOR FOR A DEEP NEURAL NETWORK WITH NORMALIZATION OR NON-LINEARITY - A simplified explanation of the abstract==


*This abstract for appeared for US patent application  number 18218426 Titled 'SEMI-PROGRAMMABLE AND RECONFIGURABLE CO-ACCELERATOR FOR A DEEP NEURAL NETWORK WITH NORMALIZATION OR NON-LINEARITY'

==Simplified Explanation==

This abstract describes a technology that uses a configurable stacked architecture to accelerate operations or layers of a deep neural network (DNN). The architecture includes a fixed function datapath with micro-execution units that perform various operations for a DNN layer. The datapath can be customized based on the specific DNN or operation being performed.


==Original Abstract Submitted==

The present disclosure relates to devices for using a configurable stacked architecture for a fixed function datapath with an accelerator for accelerating an operation or a layer of a deep neural network (DNN). The stacked architecture may have a fixed function datapath that includes one or more configurable micro-execution units that execute a series of vector, scalar, reduction, broadcasting, and normalization operations for a DNN layer operation. The fixed function datapath may be customizable based on the DNN or the operation.