Cognition and higher order reasoning in the human brain have been shown to adhere closely to probabilistic inference frameworks such as Bayesian networks that support reasoning under uncertainty. We architect a physically equivalent Bayesian network fabric with nanotechnology, employing inherently stochastic spintronic devices in unique recursive analog circuit structures that support Bayesian inference through physical fabric properties. This fabric approach results in many orders of magnitude efficiency improvements over conventional approaches and enables new cognitive applications with millions of random variables that are not possible today.
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.
The machinery of life operates on the complex interactions between genes and proteins. Attempts to capture these interactions have culminated into the study of Genetic Networks. Genetic defects lead to erroneous interactions, which in turn lead to diseases. For personalized treatment of these diseases, a careful analysis of Genetic Networks and a patient’s genetic data is required. In this work, we co-design a novel probabilistic AI model along with a reconfigurable architecture to enable personalized treatment for cancer patients.
Hardware implementations of artificial neural networks (ANNs) have become feasible due to the advent of persistent 2-terminal devices such as memristor, phase change memory, MTJs, etc. Hybrid memristor crossbar/CMOS systems have been studied extensively and demonstrated experimentally. In these circuits, memristors located at each cross point in a crossbar are, however, stacked on top of CMOS circuits using back end of line processing (BOEL), limiting scaling.
Gene Expression Networks (GENs) attempt to model how genetic information stored in the DNA (Genotype) results in the synthesis of proteins, and consequently, the physical traits of an organism (Phenotype). Deciphering GENs plays an important role in a wide range of applications from genetic studies of the origins of life to personalized healthcare. Probabilistic graphical models such as Bayesian Networks (BNs) are used to perform learning and inference of GENs from genetic data.
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.
Probabilistic machine intelligence paradigms such as Bayesian Networks (BNs) are widely used in critical real-world applications. However they cannot be employed efficiently for large problems on conventional computing systems due to inefficiencies resulting from layers of abstraction and separation of logic and memory. We present an unconventional nanoscale magneto-electric machine paradigm, architected with the principle of physical equivalence to efficiently implement causal inference in BNs.
Probabilistic graphical models are powerful mathematical formalisms for machine learning and reasoning under uncertainty that are widely used for cognitive computing. However they cannot be employed efficiently for large problems (with variables in the order of 100K or larger) on conventional systems, due to inefficiencies resulting from layers of abstraction and separation of logic and memory in CMOS implementations. In this paper, we present a magneto-electric probabilistic technology framework for implementing probabilistic reasoning functions.