 While quantization can be a viable solution to this, previous work on quantizing Transformer based models uses floating-point arithmetic during inference, thus limiting model deployment on many edge processors.
<meaning-changed>  While quantization can be a viable solution to this, previous work on quantizing Transformer based models uses floating-point arithmetic during inference, thus limiting model deployment on many edge processors.
 While quantization can be a viable solution to this, previous work on quantizing Transformer based models uses floating-point arithmetic during inference, which cannot efficiently utilize integer-only logical units such as the recent Turing Tensor Cores, or traditional integer-only ARM processors.