  We introduce a technology stack or specification describing the multiple
levels of abstraction and specialization needed to implement a neuromorphic
processor (NPU) based on the previously-described concept of AHaH Computing and
integrate it into today's digital computing systems. The general purpose NPU
implementation described here is called Thermodynamic-RAM (kT-RAM) and is just
one of many possible architectures, each with varying advantages and trade
offs. Bringing us closer to brain-like neural computation, kT-RAM will provide
a general-purpose adaptive hardware resource to existing computing platforms
enabling fast and low-power machine learning capabilities that are currently
hampered by the separation of memory and processing, a.k.a the von Neumann
bottleneck. Because understanding such a processor based on non-traditional
principles can be difficult, by presenting the various levels of the stack from
the bottom up, layer by layer, explaining kT-RAM becomes a much easier task.
The levels of the Thermodynamic-RAM technology stack include the memristor,
synapse, AHaH node, kT-RAM, instruction set, sparse spike encoding, kT-RAM
emulator, and SENSE server.
