Energy-Efficient, Near-Memory CMOS+X Architecture for Hardware Acceleration of DNNs with Application to NextG Wireless Systems

Team Members

  • Dr. Sarma Vrudhula (PI, Arizona State University)

  • Dr. Marwan Krunz (Co-PI, Univ. of Arizona)

  • Dr. Sanmukh Kuppannagari (Co-PI, Case Western Reserve University)

  • Dr. Gian Singh (post-doc, Arizona State University)

  • Ayushi Dube (Ph.D. Student, Arizona State University)

  • Abhinav Kumar (Master's Student, Arizona State University)

  • Sampad Chowdhury (Ph.D. Student, Arizona State University)

  • Arush Sharma (Ph.D. Student, Univ. of Arizona)

  • Rajan Shrestha (Ph.D. Student, Univ. of Arizona)

  • Changxin Li (Ph.D. Student, Case Reserve Western University)

Summary

Deep neural networks (DNNs) have been successfully applied in many domains, including image classification, language models, speech analysis, autonomous vehicles, wireless communications, bioinformatics, and others. Their success stems from their ability to handle vast amounts of data and infer patterns without making assumptions on the underlying dynamics that produced the data. Cloud providers operate large data centers with high-speed computers that continuously perform DNN computations, with huge energy consumption that rivals that of some industries and nations. In addition to being used in solving large-scale problems, DNNs have recently been considered for recognition applications in battery-operated systems such as smartphones and embedded devices. However, there is a critical need to improve the energy efficiency of DNNs. The main goal of this project is twofold: (1) Design and evaluate a radically innovative energy-efficient hardware/software framework for on-chip implementation of DNNs, and (2) customize this framework for new DNNs that enable real-time signal classification in next-generation wireless systems. By integrating processing elements within memory chips, the energy consumption of a DNN can be significantly reduced, and more computations can be done faster. The hardware-accelerated DNN designs provided by this project will facilitate rapid identification of wireless transmissions (e.g. radar, 5G, LTE, Wi-Fi, microwave, satellite, and others) in a shared-spectrum scenario, enabling better use of the spectrum and facilitating accurate detection of adversarial and rogue signals. To achieve 10x-100x reduction in DNN energy consumption, a holistic approach is being pursued, which encompasses: (1) new circuit designs that leverage emerging ‘CMOS+X’ technologies; (2) a novel near-memory architecture in which processing elements are seamlessly integrated with traditional Dynamic RAM (DRAM); (3) novel 3D-matrix-based per-layer DNN computations and data-layout optimizations for kernel weights; and (4) algorithms and hardware/software co-design tailored for near-real-time DNN-based signal classification in next-generation wireless systems. In addition to its research goals, the project has a comprehensive educational and outreach agendas.

Research Thrusts

  1. Thrust 1: Hardware Innovations for AI Pipelines
    2. Thrust1

    Sample Results:

    Sample result thrust 1
  2. Thrust 2: Software Acceleration (Optimized Mapping Tools)
    3. Thrust2

    Sample Results:

    Sample result thrust 2
  3. Thrust 3: Real-time Classifiers for Wireless Communications
    4. Thrust3

    Sample Results:

    Sample result thrust 3

Representation Publications