Yes binary neural networks are super interesting because they can be made much more compact in hardware than floating point ops. However there isn’t much (theoretical) advantage otherwise. Anything a circuit can do, an NN can do, and vice versa.
A circuit size penalty is already a very common technique. It’s called weight decay, where the synapses are encouraged to be as close to zero as possible. A synapse of 0 is the same as it not being there, which means the neural net parameters requires less information to specify.
Yes binary neural networks are super interesting because they can be made much more compact in hardware than floating point ops. However there isn’t much (theoretical) advantage otherwise. Anything a circuit can do, an NN can do, and vice versa.
A circuit size penalty is already a very common technique. It’s called weight decay, where the synapses are encouraged to be as close to zero as possible. A synapse of 0 is the same as it not being there, which means the neural net parameters requires less information to specify.