Associate Professor John Sartori

CSENG Electrical & Computr Eng
College of Science & Engineering
Twin Cities
Project Title: 
Parallel Simulations for Computer Architecture and Computer Aided Design; Machine Learning for Electronic Medical Records

This group is involved in two project areas using MSI:

  • Parallel Simulations for Computer Architecture and Computer Aided Design:

For several emerging applications such as wearables, internet of things, and sensor networks, energy efficiency is of utmost importance. While custom ASICs have higher energy efficiency, general-purpose embedded processors are the preferred solution for many such applications due to the evolving nature of these applications and the high costs of custom IC design. The Sartori group is focusing on new opportunities for energy efficiency enabled by detailed co-analysis of the design-level description of a processor and an application binary. Traditionally, co-analysis of the low-level hardware and details for a system has not been performed due to prohibitive costs. However, the group has developed automated analysis tools that perform unique analyses and expose new opportunities for energy efficiency. 

The researchers have created a tool that identifies the parts of a processor that can never be exercised by a particular application. As such, they can identify paths in a processor that can never be exercised for a particular workload. If the most critical paths in a processor are not exercised, then extra timing slack exists that can be exploited to reduce power or increase performance. Knowing the parts of a processor that can never be exercised by an application or application phase also allows new opportunities for aggressive power gating. The researchers are developing techniques that allow the benefits of aggressive hardware-based power gating with costs similar to those of software power gating. These techniques can provide guarantees that power gating decisions are safe without requiring hardware checking mechanisms and provide near-optimal power savings, compared to oracular control decisions. Detailed activity analysis and guarantees for a hardware-software system can also allow them to more accurately and aggressively bound the peak power requirements of the system. The gap between conventional peak power rating and application-aware peak power rating can be exploited for reduced energy and area, improved performance and throughput, and greater efficiency.

The new techniques being created require detailed analysis of a system's hardware and software. This detailed analysis relies on high-throughput parallel simulation methodologies to be performed in a reasonable amount of time. As such, high-performance parallel computing resources are necessary for this research.

  • Machine Learning for Electronic Medical Records:

The electronic medical record (EMR) is an integral part of our health care system designed to improve the quality and efficiency of medical care provided to patients. Despite its advantages in terms of improving patient care, the means of data entry into the EMR can actually detract from the care that patients receive from a medical provider. Clinical providers typically did not choose the field of medicine because they wanted to interact with a computer. Yet, multiple studies have shown that doctors are increasingly spending more time in front of a screen, navigating the EMR, than face to face with their patients. Frustration with the EMR is consistently cited as a contributor to the epidemic of physician burnout. The process of documenting the details of an encounter is tedious and time consuming, and when done in the patient room, often leads to doc-in-a-box lines of questioning, as providers attempt to fill out an electronic form in real time. Physicians who use medical scribes – assistants who listen to the conversation between patient and provider and translate into the required aspects of a clinical note in the EMR in real time – show greater job satisfaction, decreased burnout, and increased efficiency. This project aims to leverage the growing capability of speech recognition technology, artificial intelligence, and deep learning to create a virtual scribe that can automatically navigate and populate the fields in an EMR based on the conversation between a health care provider and patient. HPC resources are used to process massive medical record datasets and create machine learning techniques to classify EMR data.


Project Investigators

Hari Cherupalli
Shashank Hegde
Himanshu Shekhar Sahoo
Associate Professor John Sartori
Subhash Sethumurugan
Harini Suresh
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