Efficient multiplier-less inference of deep autoencoders on wearable healthcare systems.

This paper presents an efficient multiplier-less inference (MLI) approach of deep autoencoders (DAE) for wearable healthcare systems. It employs a novel grouped multiplier block (GMB) module to reduce computational/hardwired complexity of DAE during inference process. First, the fixed weights of DAE are transformed into sum-of-powers-of-two (SOPOT) representations so that multiplications in DAE can be realized as limited adds and shifts only. Further, a GMB is designed to reuse the partial sums in generating the products from the same inputs, which can greatly reduce the adds required. Experimental results show that our proposed MLI method is effective and efficient for wearable healthcare systems to reduce computational/hardwired complexity as well as to offer a faster software implementation.