  We propose an approximation of Echo State Networks (ESN) that can be
efficiently implemented on digital hardware based on the mathematics of
hyperdimensional computing. The reservoir of the proposed integer Echo State
Network (intESN) is a vector containing only n-bits integers (where n<8 is
normally sufficient for a satisfactory performance). The recurrent matrix
multiplication is replaced with an efficient cyclic shift operation. The
proposed intESN approach is verified with typical tasks in reservoir computing:
memorizing of a sequence of inputs; classifying time-series; learning dynamic
processes. Such architecture results in dramatic improvements in memory
footprint and computational efficiency, with minimal performance loss. The
experiments on a field-programmable gate array confirm that the proposed intESN
approach is much more energy efficient than the conventional ESN.
