Stripes: Bit-Serial Deep Neural Network Computing

The numerical representation precision required by the computations performed by Deep Neural Networks (DNNs) varies across networks and between layers of a same network. This observation motivates a precision-based approach to acceleration which takes into account both the computational structure and the required numerical precision representation. This work presents Stripes (STR), a hardware accelerator that uses bit-serial computations to improve energy efficiency and performance. Experimental measurements over a set of state-ofthe-art DNNs for image classification show that STR improves performance over a state-of-the-art accelerator from 1.35x to 5.33x and by 2.24x on average. STR's area and power overhead are estimated at 5 percent and 12 percent respectively. STR is 2.00x more energy efficient than the baseline.