Artificial Intelligence Hardware

John Oakley, Program Manager

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 comprised of five major categories:

• Architectures for Power Efficient AI Acceleration

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

• 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

    28 Projects
    23 Universities
    68 Research Scholars
    38 Faculty Researchers
    104 Liaisons
  2. This Year

    12 Project Starts
    111 Research Data
  3. Last Year

    16 Project Starts
    385 Research Data
    1 Patent Applications
  4. Since Inception

    80 Projects
    47 Universities
    221 Research Scholars
    91 Faculty Researchers
    264 Liaisons
    1,391 Research Data
    13 Patent Applications
Updated: 23-May-2024, 12:05 a.m. ET

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