The Forward–Forward (FF) algorithm, recently proposed as an alternative to backpropagation, offers layer-local training by maximizing the “goodness” of positive data and minimizing it for negative data. These files are two hardware-oriented implementations of the FF algorithm: an integer fixed-point version and a stochastic computing (SC) version based on explicit Linear Feedback Shift Registers (LFSRs). The integer approach provides accurate computation but requires costly multiply–accumulate operations, limiting its scalability to large networks. In contrast, the SC implementation replaces multipliers with simple XNOR-based logic and uses stochastic bitstreams to estimate activations and goodness values. Simulations show that while the integer version is more efficient for small and medium networks, the SC approach scales more favorably, maintaining nearly identical storage requirements and enabling massively parallel implementations with low-cost logic.