Architecting for Causal Intelligence at Nanoscale

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.

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Architecting for Artificial Intelligence with Emerging Nanotechnology

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.

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A Wafer-scale Manufacturing Pathway for Fine-grained Vertical 3D-IC Technology

Three-dimensional integrated circuits (3D-ICs) provide a feasible path for scaling CMOS technology in the foreseeable future. IMEC and IRDS roadmaps project that 3D integration is a key avenue for the IC industry beyond 2024. They project that some form of 3D-IC technology based on nanosheets/nanowires is likely to become mainstream soon.

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Realization of Four-Terminal Switching Lattices: Technology Development and Circuit Modeling

Our European Union’s Horizon-2020 project aims to develop a complete synthesis and performance optimization
methodology for switching nano-crossbar arrays that leads to the design and construction of an emerging nanocomputer.
Within the project, we investigate different computing models based on either two-terminal switches, realized with field effect

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SkyNet: Memristor-based 3D IC for Artificial Neural Networks

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.

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Fine-Grained 3D Reconfigurable Computing Fabric with RRAM

Non-volatile 3D FPGA research to date utilizes layer-by-layer stacking of 2D CMOS / RRAM circuits. On the other hand, vertically-composed 3D FPGA that integrates CMOS and RRAM circuits has eluded us, owing to the difficult requirement of highly customized regional doping and material insertion in 3D to build and route complementary p- and n-type transistors as well as resistive switches. In the layer-by-layer nonvolatile 3D FPGA, the connectivity between the monolithically stacked RRAMs and underlying CMOS circuits is likely to be limited and lead to large parasitic RCs.

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Structure Discovery for Gene Expression Networks with Emerging Stochastic Hardware

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.

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Magneto-electric Approximate Computational Circuits for Bayesian Inference

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.

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