Artificial Intelligence is becoming ubiquitous in products and services that we use daily. Although the domain of AI has seen substantial improvements over recent years, its effectiveness is limited by the capabilities of current computing technology. Recently, there have been several architectural innovations for AI using emerging nanotechnology. These architectures implement mathematical computations of AI with circuits that utilize physical behavior of nanodevices purpose-built for such computations. This approach leads to a much greater efficiency vs.

Probabilistic graphical models like Bayesian Networks (BNs) are powerful cognitive-computing formalisms, with many similarities to human cognition. These models have a multitude of real-world applications. New emerging-technology based circuit paradigms leveraging physical equivalence e.g., operating directly on probabilities vs. introducing layers of abstraction, have shown promise in raising the performance and overall efficiency of BNs, enabling networks with millions of random variables.

Neuromorphic computing mimicking the functionalities of mammalian brain holds the promise for cognitive capabilities enabling new intelligent applications. However, research efforts so far mainly focused on using analog and digital CMOS technologies to emulate neural activities, and are yet to achieve expected benefits. They suffer from limited scalability, density overhead, interconnection bottleneck and power consumption related constraints. In this paper, we present a transformative approach for neuromorphic computing with Wave Interference Functions (WIF).

We present a novel multi-valued computation framework called Wave Interference Functions (WIF), based on emerging non-equilibrium wave phenomenon such as spin waves. WIF offers new features for data representation and computation, which can be game changing for post-CMOS integrated circuits (ICs). Information encoding wave attributes inherently leads to multi-dimensional multi-valued data representation and communication. Multi-valued computation is natively supported with wave interactions, such as wave superposition or interference.

Spin Wave Functions (SPWFs) realize computation with spin waves, offering several benefits and new features over CMOS. SPWF technology potentially opens up new directions for designing microprocessors with increased capabilities over current implementations. Towards this end, as a preliminary work an 8-bit embedded processor is explored here using SPWFs and evaluated in terms of its power, area and performance using analytical estimates. A CMOS 8-bit processor implemented in an equivalent technology node is synthesized with CAD tools for comparison.