  Nature apparently does a lot of computation constantly. If we can harness
some of that computation at an appropriate level, we can potentially perform
certain type of computation (much) faster and more efficiently than we can do
with a von Neumann computer. Indeed, many powerful algorithms are inspired by
nature and are thus prime candidates for nature-based computation. One
particular branch of this effort that has seen some recent rapid advances is
Ising machines. Some Ising machines are already showing better performance and
energy efficiency for optimization problems. Through design iterations and
co-evolution between hardware and algorithm, we expect more benefits from
nature-based computing systems. In this paper, we make a case for an augmented
Ising machine suitable for both training and inference using an energy-based
machine learning algorithm. We show that with a small change, the Ising
substrate accelerate key parts of the algorithm and achieve non-trivial speedup
and efficiency gain. With a more substantial change, we can turn the machine
into a self-sufficient gradient follower to virtually complete training
entirely in hardware. This can bring about 29x speedup and about 1000x
reduction in energy compared to a Tensor Processing Unit (TPU) host.
