Thermodynamic-RAM Technology Stack

TL;DR

This paper presents the Thermodynamic-RAM (kT-RAM), a neuromorphic processor architecture based on AHaH Computing, designed to enable brain-like, low-power machine learning by integrating multiple abstraction layers into existing digital systems.

Contribution

It introduces a comprehensive technology stack for kT-RAM, detailing its components and levels to facilitate understanding and implementation of brain-inspired neural computation.

Findings

Proposes a general-purpose neuromorphic processor architecture

Defines a multi-layered technology stack for kT-RAM

Aims to reduce the von Neumann bottleneck in machine learning

Abstract

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.