This paper presents polychronous oscillatory cellular neural networks, designed for solving graph coloring problems. We propose to apply the Potts model to the four-coloring problem, using a network of locally connected oscillators under superharmonic injection locking. Based on our mapping of the Potts model to injection-locked oscillators, we utilize oscillators under divide-by-4 injection locking. Four possible states per oscillator are encoded in a polychronous fashion, where the steady state oscillator phases are analogous to the time-locked neuronal firing patterns of polychronous neurons. We apply impulse sensitivity function (ISF) theory to model and optimize the high-order injection locking of the oscillators. CMOS circuit design of a polychronous oscillatory neural network is presented, and coloring of a geographic map is demonstrated, with simulation results and design guidelines. There is good agreement between theory and Spectre simulation.

We propose a 3D architecture using post-silicon devices, such as nano-mechanical electrical switches, carbon nanotube FETs, and nanowire FETs, for future networks-on-chip (NoC). Based on such a new 3D architecture, extremely high bandwidth with very low latency can be realized. These promising features are very useful for future NoCs

Analysis and design of a tetrahedral oscillator is presented. Expressions for the startup condition and oscillation frequency are derived. A phase noise analysis of thermal and flicker noise is carried out, using the impulse sensitivity function theory. The noise contribution of each type of transistor is studied, and the dominant and negligible cyclostationary noise sources are identified. There is good agreement among the theory and Spectre simulation. Design guidelines based on the analysis are provided.

In this brief, we propose a pipeline for machine learning on CMOS Oscillator Networks (OscNet), inspired by human visual system development. By relying solely on forward propagation, OscNet is energy efficient and preserves biological plausibility. Our simulations confirm the effectiveness of the proposed architectures, and experimental results demonstrate that the Hebbian-learning pipeline on OscNet matches traditional machine learning algorithms, underscoring its promise as an energy efficient and high performance new computational paradigm. The repository for OscNet family is:.

This paper presents quantum computing gate emulation using polychronous oscillatory cellular neural networks. The oscillatory neural network consists of locally interacting ring oscillators and control switches, in a standard CMOS process. We apply impulse sensitivity function (ISF) theory to model the injection locking of the oscillators. We show the design of universal quantum gate emulation with oscillator circuits. Quantum circuits for the Greenberger–Horne–Zeilinger State and W State are emulated using CMOS oscillators and switches. Spectre simulation results agree with the quantum circuit theory.