합성곱 인공 신경망 처리기의 빠른 연산 블록 생성 기법

This work addresses the problem of generating a fast arithmetic circuit for each of the node computations of convolutional neural network (CNN). Once a training phase is done in CNN, the computation of each node can be expressed as a sum of multiplications with constant inputs (i.e., with known weights obtained from the training phase). Consequently, we can convert the multiplications into a sum of multiple addends, thereby producing an expression of sum of addends for the node computation, from which we generate a fast carry-save-adder (CSA) implementation structure for the expression. Through gate-level implementation of our proposed technique, we are able to reduce the node computation time by 8.8% on average while a little increase in power consumption, which is mainly caused by our prototyping of incompletely optimized CSA circuits in comparison with the commercial CSAs.