Nonsilicon, Non-von Neumann Computing—Part I [Scanning the Issue]

Abstract

Majority-inverter graphs (MIGs) are a logic representation with remarkable algebraic and Boolean properties that enable efficient logic optimizations beyond the capabilities of traditional logic representations. Further, since many nano-emerging technologies, such as quantum-dot cellular automata (QCA) or spin torque majority gates (STMG), are inherently majority-based, MIGs serve as a natural logic representation to map into these technologies. So far, MIG optimization methods predominantly target to reduce the depth of the logic networks, corresponding to low delay implementations in the respective technologies. In this paper, we introduce several methods to optimize the size of MIGs. They can be applied such that the depth of the logic network is preserved; therefore our methods have a direct effect on the physical area, without worsening the delay. Some methods are inspired by existing size optimization algorithms for non-majority-based logic networks, others make explicit use of the majority function and its properties. All methods are Boolean-in contrast to algebraic optimization methods-which has a positive effect on the quality but challenges their implementation. Our experiments show that using our methods the size of MIGs in the EPFL combinational benchmark suite can be reduced by up to 7.12%. When mapped to QCA and STMG technologies we reduce the average area-delay-energy product by 2.31% and 2.07%, respectively.

100,000-spin coherent Ising machine

“…With the apparent saturation of the progress in digital computers, new types of computers based on nonsilicon physical systems are highly anticipated. Unlike current digital computers based on Turing machine procedures, these computers use time evolution of physical systems to perform tasks such as speech and image recognition, data mining, and optimization ( 1 ). On the basis of a computing paradigm of “let physics do computation,” they include quantum computers ( 2 ), quantum annealers ( 3 ), neural networks ( 4 ), and reservoir computers ( 5 ), implemented with various physical systems such as superconducting qubits ( 6 , 7 ), trapped ions ( 8 ), and photonics ( 9 – 12 ).…”