I will discuss a novel computing paradigm we named memcomputing inspired by the operation of our own brain. Memcomputing - computing using memory circuit elements or memelements - satisfies important physical requirements: (i) it is intrinsically massively parallel, (ii) its information storing and computing units are physically the same, and (iii) it does not rely on active elements as the main tools of operation. I will then introduce the notion of universal memcomputing machines (UMMs) as a class of general-purpose computing machines based on systems with memory. We have shown 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 have proved that UMMs can solve NP-complete problems in polynomial time, and as an example I will provide the polynomial-time solution of the subset-sum problem when implemented in hardware. Even though we have not proved NP=P, the practical implementation of these UMMs would represent a paradigm shift from present von Neumann architectures bringing us closer to brain-like neural computation. In fact, I will discuss a practical CMOS-compatible realization of this computing paradigm that uses memcapacitors and we have named Dynamic Computing Random Access Memory (DCRAM).
I will discuss a novel computing paradigm we named memcomputing inspired by the operation of our own brain. Memcomputing - computing using memory circuit elements or memelements - satisfies important physical requirements: (i) it is intrinsically massively parallel, (ii) its information storing and computing units are physically the same, and (iii) it does not rely on active elements as the main tools of operation. I will then introduce the notion of universal memcomputing machines (UMMs) as a class of general-purpose computing machines based on systems with memory. We have shown 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 have proved that UMMs can solve NP-complete problems in polynomial time, and as an example I will provide the polynomial-time solution of the subset-sum problem when implemented in hardware. Even though we have not proved NP=P, the practical implementation of these UMMs would represent a paradigm shift from present von Neumann architectures bringing us closer to brain-like neural computation. In fact, I will discuss a practical CMOS-compatible realization of this computing paradigm that uses memcapacitors and we have named Dynamic Computing Random Access Memory (DCRAM).
