Micron Technology, Inc.
FAULT TOLERANT ARTIFICIAL NEURAL NETWORK COMPUTATION IN DEEP LEARNING ACCELERATOR HAVING INTEGRATED RANDOM ACCESS MEMORY

Abstract:

Systems, devices, and methods related to a Deep Learning Accelerator and memory are described. For example, an integrated circuit device may be configured to execute instructions with matrix operands and configured with random access memory (RAM) to store parameters of an artificial neural network (ANN). The device can generate random bit errors to simulate compromised or corrupted memory cells in a portion of the RAM accessed during computations of a first ANN output. A second ANN output is generated with the random bit errors applied to the data retrieved from the portion of the RAM. Based on a difference between the first and second ANN outputs, the device may adjust the ANN computation to reduce sensitivity to compromised or corrupted memory cells in the portion of the RAM. For example, the sensitivity reduction may be performed through ANN training using machine learning.