Many of these algorithms are bandwidth- or cache-limited on modern machines, so you can get significant speedup by storing your data in fewer bytes, even if you expand it in registers before actually doing computation on it.
https://software.intel.com/sites/landingpage/IntrinsicsGuide...
The result is that Winograd convolutions can achieve an effective FMA rate of twice the peak rate of the CPU.
The Winograd transform reduces the required number of FMAs by a factor of 5x, but you can only do FMAs at half peak rate (because you are bandwidth limited), so you come out ahead by a factor of 2.5x in theory (2x in practice).
Without fp16, that 2x advantage would be lost.