A New Constant Coefficient Multiplier for Deep Neural Network Accelerators

Deep Neural Network accelerators are a key component in enabling the penetration of Artificial Intelligence, through their integration at the edge. However, due to the requirement of hierarchical learning in deep networks, the number of layers and parameters is very large. In this work, an optimization for the multiplier used as part of the MAC unit is proposed: the weights can be considered constant once downloaded to the accelerator, as these do not change during the inference process. Under these circumstances, pre-processing steps which make the computation of the product faster are introduced in this work. Since this step is performed only once, the overhead due to the pre-processing unit is minimal, while optimizing the multiplier in thousands of MAC units across the architecture. Experimental results show that the variable part of the SCM design that has been proposed in this work serves well when a tradeoff between power, area and timing is taken into consideration. The results proposed are a comparison of the variable parts of both the KCM and SCM designs, along with the default multiplier, as the primary idea behind this work is the one time use of the constant multiples generator.