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Six Areas in AI Hardware Research

Areas Studied by IBM Research’s AI Hardware

The company that owns the most patents related to machine learning techniques, and thus arguably rather to a large extent some of the largest intellectual property in the field of artificial intelligence — that company is IBM.

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Topmost banner of the hardware section of the IBM website
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Six areas outlined by IBM within their AI hardware research.
  1. Analog AI Cores enable in-memory storage and processing of data to speed computation and yield exponential gains in computational efficiency.
    -Unveiling Analog Memory-based Technologies to Advance AI at VLSI
    -The Future of AI Needs Better Compute: Hardware Accelerators Based on Analog Memory Devices
    -IBM Scientists Demonstrate Mixed-Precision In-Memory Computing for the First Time; Hybrid Design for AI Hardware
    -IBM Scientists Demonstrate In-memory Computing with 1 Million Devices for Applications in AI
    -Steering Material Scientists to Better Memory Devices
    -Dual 8-Bit Breakthroughs Bring AI to the Edge
  2. Heterogenous integration. AI applications drive the need for a system level optimization of AI Hardware through Heterogeneous Integration of Accelerators, Memory and CPU. The AI Hardware center will focus on interconnect solutions to enable high speed, high bandwidth connectivity between the different components.
  3. Quantum computing for machine learning. Quantum computing has emerged as a new computing paradigm with the potential of addressing problems that are intractable for today’s classical computers. Recently, Havlicek demonstrated the use of a superconducting quantum processor to perform a traditional machine learning classification task. We believe there is significant potential in using quantum computing for machine learning by exploiting the exponentially large quantum state space.
    Researchers Put Machine Learning on Path to Quantum Advantage
    Test a quantum classifier
  4. Machine intelligence is very different from machine learning. Machine intelligence involves the use of fast associative reasoning to mimic human intelligence. IBM Research is exploring machine intelligence by using the brain’s neocortex as a model for developing flexible systems that learn continuously — and without human supervision. A recent paper discusses unique structures in the human brain called filopodia that may have a role in fast learning.
    Filopodia: A Rapid Structural Plasticity Substrate for Fast Learning
  5. AI optimized systems.
    Reaching the Summit: The next milestone for HPC
    Distributed Deep Learning with IBM DDL and TensorFlow NMT

Written by

AI Policy and Ethics at www.nora.ai. Student at University of Copenhagen MSc in Social Data Science. All views are my own. twitter.com/AlexMoltzau

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