Artificial Intelligence Hardware

David Yeh, Director

Creating new highly-efficient AI platforms to enable neuro-inspired, cognitive and learning abilities which will be required to address the vast range of future data types and workloads as intelligence is enabled from edge devices to the cloud.


This research program highlights the importance of hardware in pushing the frontiers of artificial intelligence across a broad spectrum of applications from the edge to the cloud. The program has roots in two previous efforts: System Level Design (SLD) and Efficiency and Performance for Connectivity Constrained Computing (EP3C).

Research Focus

The AI Hardware research program is sub-divided into six major categories:

• Novel Architectures for Accelerating AI Computation

• Modeling and Simulation/Emulation of AI Hardware for Early System Exploration

• Power Efficient AI Hardware System Design

• HW/SW Co-design of AI Compute Systems

• Fairness, Robustness, Privacy, and Explainability of Models and Algorithms for AI Hardware

• Interplay of AI and System Architecture/Microarchitecture Design

In each category, there may be research covering large systems to small (datacenter and the edge/end node) as well as a broad range of applications, including high-performance processors for data centers, automotive, industrial, mobile computing and communication, and healthcare.

AIHW Metrics

  1. Current

    32 Research Tasks
    24 Universities
    65 Students
    38 Faculty Researchers
    98 Liaison Personnel
  2. This Year

    21 Task Starts
    144 Research Publications
  3. Last Year

    3 Task Starts
    70 Research Publications
  4. Since Inception

    36 Research Tasks
    28 Universities
    80 Students
    44 Faculty Researchers
    111 Liaison Personnel
    283 Research Publications
Updated: 22-Sep-2020, 12:05 a.m. ET

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