Multispeculative addition applied to datapath synthesis

Addition is the key arithmetic operation in most digital circuits and processors. Therefore, their performance and other parameters, such as area and power consumption, are highly dependent on the adders' features. In this paper, we present multispeculation as a way of increasing adders' performance with a low area penalty. In our proposed design, dividing an adder into several fragments and predicting the carry-in of each fragment enables computing every addition in two very short cycles at the most, with 99% or higher probability. Furthermore, based on multispeculation principles, we propose a new strategy for implementing addition chains and hiding most of the penalty cycles due to mispredictions, while keeping at the same time the resource sharing capabilities that are sought in high-level synthesis. Our results show that it is possible to build linear and logarithmic adders more than 4.7× and 1.7 × faster than the nonspeculative case, respectively. Moreover, this is achieved with a low area penalty (38% for linear adders) or even an area reduction (-8% for logarithmic adders). Finally, applying multispeculation principles to signal processing benchmarks that use addition chains will result in 25% execution time reduction, with an additional 3% decrease in datapath area with respect to implementations with logarithmic fast adders.