Design Principles for Lifelong Learning AI Accelerators
Dhireesha Kudithipudi Anurag Daram Abdullah M. Zyarah Fatima Tuz Zohora James B. Aimone Angel Yanguas-Gil Nicholas Soures Emre Neftci Matthew Mattina Vincenzo Lomonaco Clare D. Thiem Benjamin Epstein
Lifelong learning - an agent's ability to learn throughout its lifetime - is
a hallmark of biological learning systems and a central challenge for
artificial intelligence (AI). The development of lifelong learning algorithms
could lead to a range of novel AI applications, but this will also require the
development of appropriate hardware accelerators, particularly if the models
are to be deployed on edge platforms, which have strict size, weight, and power
constraints. Here, we explore the design of lifelong learning AI accelerators
that are intended for deployment in untethered environments. We identify key
desirable capabilities for lifelong learning accelerators and highlight metrics
to evaluate such accelerators. We then discuss current edge AI accelerators and
explore the future design of lifelong learning accelerators, considering the
role that different emerging technologies could play.