Tensor cores are specialized units designed for efficient 16-bit multiplication and 32-bit addition, enhancing performance in AI applications. While inference is generally easier and faster, the challenge remains in applying reduced precision techniques effectively during training without sacrificing accuracy. Key strategies include implementing a global switch for precision representation and maintaining a master copy of weights in 32-bit format for optimal training results.
