Approximate Computing

Hardware-accelerated Simulation-based Inference of Stochastic Epidemiology Models for COVID-19

Epidemiology models are central in understanding and controlling large scale pandemics. Several epidemiology models require simulation-based inference such as Approximate Bayesian Computation (ABC) to fit their parameters to observations. ABC inference is highly amenable to efficient hardware acceleration. In this work, we develop parallel ABC inference of a stochastic epidemiology model for COVID-19.

PDF icon PDF (In submission, arXiv preprint)2.27 MB

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.

PDF icon PDF (In Press)1.32 MB

Accelerating Simulation-based Inference with Emerging AI Hardware

Developing models of natural phenomena by capturing their underlying complex interactions is a core tenet of various scientific disciplines. These models are useful as simulators and can help in understanding the natural processes being studied. One key challenge in this pursuit has been to enable statistical inference over these models, which would allow these simulation-based models to learn from real-world observations. Recent efforts, such as Approximate Bayesian Computation (ABC), show promise in performing a new kind of inference to leverage these models.

PDF icon PDF.pdf594.81 KB

Reconfigurable Probabilistic AI Architecture for Personalized Cancer Treatment

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.

PDF icon PDF4.86 MB

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.

PDF icon PDF534.29 KB

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.

PDF icon PDF986.8 KB
Subscribe to RSS - Approximate Computing