  This paper proposes the design and implementation strategy of a novel
computing architecture, the Factor Machine. The work is a step towards a
general-purpose parallel system operating in a non-sequential manner,
exploiting processing/memory co-integration and replacing the traditional
Turing/von Neumann model of a computer system with a framework based on
"factorised computation". This architecture is inspired by neural information
processing principles and aims to progress the development of brain-like
machine intelligence systems, through providing a computing substrate designed
from the ground up to enable efficient implementations of algorithms based on
relational networks. The paper provides a rationale for such machine, in the
context of the history of computing, and more recent developments in
neuromorphic hardware, reviews its general features, and proposes a
mixed-signal hardware implementation, based on using analogue circuits to carry
out computation and localised and sparse communication between the compute
units.
