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In the current landscape of large models, the Transformer stands as acornerstone, playing a pivotal role in shaping the trajectory of modern models.However, its application encounters challenges attributed to the substantialcomputational intricacies intrinsic to its attention mechanism. Moreover, itsreliance on high-precision floating-point operations presents specific hurdles,particularly evident in computation-intensive scenarios such as edge computingenvironments. These environments, characterized by resource-constrained devicesand a preference for lower precision, necessitate innovative solutions.  To tackle the exacting data processing demands posed by edge devices, weintroduce the Bitformer model, an inventive extension of the Transformerparadigm. Central to this innovation is a novel attention mechanism thatadeptly replaces conventional floating-point matrix multiplication with bitwiseoperations. This strategic substitution yields dual advantages. Not only doesit maintain the attention mechanism's prowess in capturing intricate long-rangeinformation dependencies, but it also orchestrates a profound reduction in thecomputational complexity inherent in the attention operation. The transitionfrom an $O(n^2d)$ complexity, typical of floating-point operations, to an$O(n^2T)$ complexity characterizing bitwise operations, substantiates thisadvantage. Notably, in this context, the parameter $T$ remains markedly smallerthan the conventional dimensionality parameter $d$.  The Bitformer model in essence endeavors to reconcile the indomitablerequirements of modern computing landscapes with the constraints posed by edgecomputing scenarios. By forging this innovative path, we bridge the gap betweenhigh-performing models and resource-scarce environments, thus unveiling apromising trajectory for further advancements in the field.

Bitformer: An efficient Transformer with bitwise operation-based attention for Big Data Analytics at low-cost low-precision devices