Future of Computing: Universal Memcomputing Machines

http://arxiv.org/abs/1405.0931

We introduce the notion of universal memcomputing machines (UMMs): a class of brain-inspired general-purpose computing machines based on systems with memory, whereby processing and storing of information occur on the same physical location. We analytically prove that the memory properties of UMMs endow them with universal computing power - they are Turing-complete -, intrinsic parallelism, functional polymorphism, and information overhead, namely their collective states can support exponential data compression directly in memory. We also demonstrate that a UMM has the same computational power as a non-deterministic Turing machine, namely it can solve NP--complete problems in polynomial time. However, by virtue of its information overhead, a UMM needs only an amount of memory cells (memprocessors) that grows polynomially with the problem size. As an example we provide the polynomial-time solution of the subset-sum problem and a simple hardware implementation of the same. The practical realization of these UMMs would represent a paradigm shift from present von Neumann architectures bringing us closer to brain-like neural computation.

>>5

On GPU, each shader unit has it's own ALU and memory, so "processing and storing of information occur on the same physical location."

These architectures basically trade frequency for bandwidth. The problem as usual comes with communication between all these computation units. Mammal brain solves communication problem by supplying each neuron with a huge dendritic tree, but that solution is infeasible to replicate on contemporary hardware, because human brain for example requires about 8500000000000000 (8.5 quadrillion) bytes of such memory (given that each neuron has about 100000 connections).

This leads me to conclusion, that modern GPUs use their transistors the best currently possible way.