Neural network processing unit including approximate multiplier and system on chip including the same

A system on chip, comprising:

• one or more semiconductor intellectual property cores (IPs);
• processing circuitry configured to output at least a first control signal and a second control signal to at least one neural processor (NPU), the first and second control signals respectively configured to enable a selection by the at least one NPU between a training mode and an inference mode;
• and
• a neural network processing unit configured implement a neural network and to receive input data from the one or more IPs, and perform a neural network computation based on the input data and a plurality of weights, the neural network processing unit including a plurality of neural processors (NPUs) and the plurality of NPUs include the at least one NPU,
• wherein the at least one NPU of the plurality of NPUs is configured to
• switch to a fixed-point approximate multiplication training mode in response to receiving the first control signal,
• receive a first value and a second value while in the fixed-point approximate multiplication training mode,
• perform a fixed-point approximate multiplication operation on the first value and the second value in response to receiving the first value and the second value while in the fixed-point approximate multiplication training mode,
• review an output value for a loss of accuracy, the output value including a result of the approximate multiplication operation and the review including
• perform a stochastic rounding operation on the output value to output a post activation regarding the output of the approximate multiplication operation, and
• determine, based on the result of the stochastic rounding operation, the loss of accuracy for the output value, and
• train the neural network by tuning a parameter of the at least one NPU based on the determined loss, and
• wherein the at least one NPU of the plurality of NPUs is configured to
• select a general multiplication inference mode in response to receiving the second control signal,
• receive an input value while in the general multiplication inference mode, and
• perform a general multiplication operation based on the input value and the tuned parameter in response to receiving the input value while in the general multiplication inference mode.

A neural network processing unit configured to perform a training operation based on one or more instances of training data and a plurality of weights in a training mode or to perform an inference operation based on one or more instances of input data and the plurality of weights in an inference mode, the neural network processing unit comprising:

• a controller configured to output at a first control signal and a second control signal to at least one neural processor (NPU), the first and second control signals respectively configured to enable a selection by the at least one NPU between the training mode and the inference mode;
• and
• a plurality of neural processors (NPUs) including the at least one NPU,
• wherein the at least one NPU of the plurality of NPUs is configured to,
• switch to a fixed-point approximate multiplication training mode in response to receiving the first control signal,
• receive a first value and a second value while in the fixed-point approximate multiplication training mode,
• perform a fixed-point approximate multiplication operation on the first value and the second value in the training mode in response to receiving the first value and the second value while in the fixed-point approximate multiplication training mode,
• perform an addition operation on an output value of the fixed-point approximate multiplication operation and a third value,
• accumulate an output value of the fixed-point approximate multiplication operation,
• review an output value for a loss of accuracy, the output value including an accumulation value output based on the accumulating and the review including
• performing a stochastic rounding operation on an accumulation value output based on the accumulating to output a post activation regarding the accumulation value,
• determining, based on a result of the stochastic rounding operation, the loss of accuracy for the output value, and
• train the neural network by tuning a parameter of the at least one NPU based on the determined loss, and
• wherein the at least one NPU of the plurality of NPUs is configured to
• select a general multiplication inference mode in response to receiving the second control signal,
• to receive an input value while in the general multiplication inference mode, and
• perform a general multiplication operation based on the input value and the tuned parameter in response to receiving the input value while in the general multiplication inference mode.