Energy Efficient Machine Learning Hardware Co-design

From Center for Integrated Circuits and Devices Research (CIDR)
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This  component  project  of  the  CIDR  program tackles  the  co-design  of  energy-efficient  machine  learning algorithms and hardware. Methodologies to integrate machine learning on-chip for distributed data processing, network lifespan improvement and security will be explored. These methodologies will likewise pave the way for automated hardware generation for the accelerator needed to perform these tasks.

Personnel

Project Leader Anastacia B. Alvarez, PhD
Senior Technical Specialist Sherry Joy Alvionne S. Baquiran
Technical Specialist Fredrick Angelo Galapon
Allen Jason Tan
Lawrence Roman Quizon
Administrative Officer Maria Luz Limun
Technical Aide Patrick Jake Valdez [Y1Q1-Y1Q3]

Andrei Gabriel Ay-ay [Y1-Q4]

Project Staff Ryan Albert Antonio
Rhandley D. Cajote, PhD
John Francis Chan
Student Affiliate Joenard Matanguihan
Randolf Tamayo

Activities

The project will have four (4) major activities:

  1. Design and implementation WSN machine learning for clustering and routing
  2. ISA-optimization of RISC-V processor for machine learning
  3. Design, implementation, and verification of a proof-of-concept distributed learning in 28nm FDSOI CMOS technology.
  4. Design and implementation of a proof-of-concept security module using physically unclonable functions (PUF)

Resources