👉 Beam computing is a parallel processing technique designed to optimize the execution of machine learning models by maintaining multiple hypotheses or "beams" during inference. Each beam represents a set of candidate solutions that could potentially be the optimal output, and these beams are evaluated simultaneously to identify the most promising ones. By keeping a limited number of the strongest beams and combining their results, beam computing reduces the computational overhead associated with exhaustive search methods while maintaining high accuracy. This approach is particularly effective for complex models like transformers, where the number of possible configurations can be vast. Beam computing balances efficiency and performance by pruning less likely solutions early in the process, thus accelerating inference without significantly compromising model quality.
