Reservoir computing (RC) features with the rich computational dynamics is a kind of powerful machine learning paradigm that is well suited for non-linear time-series prediction and classification problems. However, this impressive performance comes with a cost of complex arithmetic operations and high memory usage that make it significantly challenging to deploy on embedded systems. Solutions based on CPU and/or GPU-based designs, provides flexibility but suffers from a lack of efficiency in terms of power, performance, and area (PPA). Although hardware-accelerated solutions can improve efficiency, it takes longer design cycles and is time-consuming. Furthermore, it may happen that design spec requires run change due to the fact that the network is retrained with the new data set to improve the performance. It leads to extra effort in the redesign of the hardware-accelerated solution. This preliminary work presents the design and implementation of a hardware generator for RC-ESNs (echo state networks) to tackle the problem. The proposed methodology is demonstrated by various offline-trained network parameters and topologies. Compared to existing solutions, the proposed framework provides scalability with the support of DSE in agile hardware design.