Thanks for your reply.
I don’t mean drop it after the model trained with it. But mean there is no bias at all in training so the error backpropgate will only update the weights rather than toghther with bias. I know it takes small memory usage but sometimes the layout of circuit or other implementation condition just more simplifed without it. I am not sure about it. But i do have one example in which only the weights are updated and there is no bias and it works well. I think just remove it can cause some problems. There is one paper says that DL may don’t need bias due to the weights are already enough but i just can’t find that paper now. I will keep looking for it.