Abstract: Broadly speaking, the present disclosure generally relate to an electronic device for accelerating machine learning, ML, model computations is provided. The electronic device comprises: a first processor configured to: generate a query matrix, a key matrix, and a value matrix by performing Fast Fourier Transform (FFT) and butterfly linear transform on at least one input matrix, and a second processor configured to: perform a first matrix multiplication between the query matrix and the key matrix, perform a softmax operation on the result of the first matrix multiplication, and perform a second matrix multiplication between the result of the softmax operation and the value matrix.

Abstract: A method of predicting performance of a hardware arrangement or a neural network model includes: obtaining one or more of a first hardware arrangement or a first neural network model, obtaining a first graphical model comprising a first plurality of nodes corresponding to the obtained first hardware arrangement or the obtained first neural network model, wherein each node of the first plurality of nodes corresponds to a respective component or device of the first plurality of interconnected components or devices or a respective operation of the first plurality of operations; extracting, based on the first graphical model, a first graphical representation of the obtained first hardware arrangement or the obtained first neural network model; predicting, based on the first graphical representation, performance of the obtained first hardware arrangement or the obtained first neural network model; and outputting the predicted performance.

Abstract: Disclosed are an electronic device and a method for controlling thereof.