Stochastic computing has been used in recent years to create designs with significantly smaller area by harnessing unary encoding of data. However, the low area advantage comes at an exponential price in latency, making the area × delay cost unattractive. In this paper, we present a novel method which uses a hybrid binary / unary representation to perform computations. We first divide the input range into a few sub-regions, perform unary computations on each sub-region individually, and finally pack the outputs of all sub-regions back to compact binary. Moreover, we propose a synthesis methodology and a regression model to predict an optimal or sub-optimal design in the design space. The proposed method is especially well-suited to FPGAs due to the abundant availability of routing and flip-flop resources. To the best of our knowledge, we are the first to show a scalable method based on the principles of stochastic computing that can beat conventional binary in terms of a real cost, i.e., area×delay. Our method outperforms the binary and fully unary methods on a number of functions and on a common edge detection algorithm. In terms of area × delay cost, our cost is on average only 2.51% and 10.2% of the binary for 8- and 10-bit resolutions, respectively. These numbers are 2-3 orders of magnitude better than the results of traditional stochastic methods. Our method is not competitive with the binary method for high-resolution oscillating functions such as sin(15x).