AIHW
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.

Overview

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.

Metric data displayed below is accurate through December 13, 2022. We appreciate your patience as we look to upgrade our reporting system and continue to provide ways for you to measure the success of your investments.

AIHW Metrics

  1. Last Year

    244 Research Publications
    6 Patent Applications
  2. Since Inception

    78 Research Tasks
    44 Universities
    152 Students
    86 Faculty Researchers
    185 Liaison Personnel
    837 Research Publications
    12 Patent Applications
Updated: 29-Nov-2023, 12:05 a.m. ET

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