The SVE FEXPA instruction speeds up the computation of exponential functions by implementing table lookup and bit manipulation. The exponential function is the core of the Softmax function that, with the shift toward Generative AI, has become a critical component of modern neural network architectures.
An implementation of the exponential function based on FEXPA can achieve a specified target precision using a polynomial of lower degree than alternative implementations. SME support for FEXPA lets you embed the exponential approximation directly into the matrix computation path, which translates into:
These improvements make exponential-heavy workloads significantly faster on Arm CPUs.