Abstract: A parallel system for performing LMS coefficient adaptation includes a data memory, a tap memory, and two or more LMS hardware units. The LMS hardware units utilize data stored in the data memory and coefficients stored in the tap memory for performing multiple LMS coefficient adaptations in parallel.

Abstract: A method for performing convolution on one or more first vectors including multiple vector elements using one or more second vectors. The method includes identifying one or more of the multiple vector elements of the one or more first vectors that do not have a value of zero, and convoluting the one or more first vectors with the one or more second vectors for the identified one or more of the multiple vector elements of the one or more first vectors that do not have a value of zero.

Abstract: A system and method for accelerating least-mean-square algorithm-based coefficient adaptation which executes in one machine clock cycle one tap of the least-mean-square algorithm including data fetch, coefficient fetch, coefficient adaptation, convolution, and write-back of a new coefficient vector. A data memory stores an input signal. A coefficient memory stores a coefficient vector. A multiplication and accumulation unit reads the input signal from the data memory and the coefficient vector from the coefficient memory to perform convolution.