A new accelerator chip called 'Hiddenite' that can achieve state-of-the-art accuracy in the calculation of sparse 'hidden neural networks' with lower computational burdens has now been developed. By employing the proposed on-chip model construction, which is the combination of weight generation and 'supermask' expansion, the Hiddenite chip drastically reduces external memory access for enhanced computational efficiency.
