Tesla, Inc.

Advanced Micro Devices Corporation

Benjamin Floering

Emil Talpes

Peter Joseph Bannon

Debjit Das Sarma

Kevin Altair Hurd

Christopher Hsiong

Kevin Michael Lepak

Kai Troester

Magiting M Talisayon

Hongwen Gao

Hrishikesh Murukkathampoondi

A system for handling errors in a neural network includes a neural network processor for executing a neural network associated with use of a vehicle. The neural network processor includes an error detector configured to detect a data error associated with execution of the neural network and a neural network controller configured to receive a report of the data error from the error detector. In response to receiving the report, the neural network controller is further configured to signal that a pending result of the neural network is tainted, without terminating execution of the neural network.

System and method for handling errors in a vehicle neural network processor

A system for handling errors in a neural network includes a neural network processor for executing a neural network associated with use of a vehicle. The neural network processor includes an error detector configured to detect a data error associated with execution of the neural network and a neural network controller configured to receive a report of the data error from the error detector. In response to receiving the report, the neural network controller is further configured to signal that a pending result of the neural network is tainted without terminating execution of the neural network.

Systems, apparatuses, and methods for implementing a fastpath microcode sequencer are disclosed. A processor includes at least an instruction decode unit and first and second microcode units. For each received instruction, the instruction decode unit forwards the instruction to the first microcode unit if the instruction satisfies at least a first condition. In one implementation, the first condition is the instruction being classified as a frequently executed instruction. If a received instruction satisfies at least a second condition, the instruction decode unit forwards the received instruction to a second microcode unit. In one implementation, the first microcode unit is a smaller, faster structure than the second microcode unit. In one implementation, the second condition is the instruction being classified as an infrequently executed instruction. In other implementations, the instruction decode unit forwards the instruction to another microcode unit responsive to determining the instruction satisfies one or more other conditions.

Fastpath microcode sequencer

A method for determining event based energy weights for digital power estimation includes obtaining a reference energy value corresponding to a power consumed by at least a portion of an integrated circuit (IC) device during operation. The method includes determining and selecting a subset of signals from a set of all signals within the IC that correlates to energy use within the IC. The method includes determining an activity factor of each signal in the subset by monitoring each signal while simulating execution of a particular set of instructions. The method includes determining a weight factor or at least an approximation of a weight factor for each signal in the subset by solving within a predetermined accuracy, a multivariable equation in which the reference energy value equals a weighted sum of the activity of the signals of the selected subset multiplied by their respective weight factors.

Method of determining event based energy weights for digital power estimation

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San Jose, CA, United States of America

Benjamin Floering