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US 11,537,859 B2Granted
Flexible precision neural inference processing unit
AssigneeInternational Business Machines Corporation
InventorsAndrew S. Cassidy, Rathinakumar Appuswamy, John V. Arthur, Pallab Datta, Steve Esser, Myron D. Flickner, Jeffrey McKinstry, Dharmendra S. Modha, Jun Sawada, Brian Taba
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Abstract
Neural inference chips are provided. A neural core of the neural inference chip comprises a vector-matrix multiplier; a vector processor; and an activation unit operatively coupled to the vector processor. The vector-matrix multiplier, vector processor, and/or activation unit is adapted to operate at variable precision.
Claims20
A neural inference chip comprising a neural core, the neural core comprising:
- a vector-matrix multiplier adapted to
- receive a weight matrix having a weight matrix precision,
- receive an input activation vector having an input activation vector precision, and
- compute a partial sum vector by multiplying the input activation vector by the weight matrix, the partial sum vector having a partial sum vector precision;
- a vector processor adapted to
- receive one or more partial sum vector from one or more vector source, the one or more vector source including the vector-matrix multiplier, and
- perform one or more vector function on the one or more partial sum vector to yield a vector processor output vector, the vector processor output vector having a precision equal to the partial sum vector precision;
- and
- an activation unit operatively coupled to the vector processor and adapted to
- apply an activation function to the vector processor output vector, yielding an output activation vector having an output activation precision, wherein the vector-matrix multiplier, vector processor, and/or activation unit is adapted to operate at variable precision.
7 more elements
A method comprising:
- receiving a weight matrix having a first precision;
- receiving an activation vector having the first precision;
- computing a vector-matrix multiplication of the weight matrix and the activation vector,
- yielding a partial sum vector a second precision;
- performing one or more vector functions on the partial sum vector to yield a vector processor output vector having the second precision;
- and
- applying an activation function to the vector processor output vector, yielding an output activation vector having a third precision, wherein at least one of the first, second, and third precision is varied at runtime.
4 more elements
Dependencies are inferred from the claim text.