[R] Ternary neural networks as a path to more efficient AI - is (+1, 0, -1) weight quantization getting serious research attention?

I've been reading about ternary weight quantization in neural networks and wanted to get a sence of how seriously the ML research community is taking this direction. The theoretical appeal seems clear: ternary weights (+1, 0, -1) cut model size and inference cost a lot compared to full-precision or even binary networks, while keeping power than strict binary. Papers like TWN (Ternary Weight Networks) from 2016 and some newer work suggest this is a real path for efficient inference. What I've been less clear on is the.