Trinary is an efficient way of storing lots of -1/0/1 machine learning model weights. But as soon as you load it into memory, you need RAM that can store the sa

Trinary is an efficient way of storing lots of -1/0/1 machine learning model weights. But as soon as you load it into memory, you need RAM that can store the same thing (or you're effectively losing the benefits: storage is cheap). So now you need trinary RAM, which as it turns out, isn't great for doing normal general purpose computation with. Integers and floats and boolean values don't get stored efficiently in trinary unless you toss out power of two sized values. CPU circuitry becomes more complicated to add/subtract/multiply those values. Bitwise operators in trinary become essentially impossible for the average IQ engineer to reason about. We need all new IAs, assembly languages, compilers, languages that can run efficiently without the operations that trinary machines can't perform well, etc. So do we have special memory and CPU instructions for trinary data that lives in a special trinary address space, separate from traditional data that lives in binary address space? No, the juice isn't worth the squeeze. There's no compelling evidence this would make anything better overall: faster, smaller, more energy efficient. Every improvement that trinary potentially offers results in having to throw babies out with the bathwater. It's fun to think about I guess, but I'd bet real money that in 50 years we're still having the same conversation about trinary.