Neuromorphic Co-Design as a Game
Craig M. Vineyard William M. Severa James B. Aimone
Co-design is a prominent topic presently in computing, speaking to the mutual
benefit of coordinating design choices of several layers in the technology
stack. For example, this may be designing algorithms which can most efficiently
take advantage of the acceleration properties of a given architecture, while
simultaneously designing the hardware to support the structural needs of a
class of computation. The implications of these design decisions are
influential enough to be deemed a lottery, enabling an idea to win out over
others irrespective of the individual merits. Coordination is a well studied
topic in the mathematics of game theory, where in many cases without a
coordination mechanism the outcome is sub-optimal. Here we consider what
insights game theoretic analysis can offer for computer architecture co-design.
In particular, we consider the interplay between algorithm and architecture
advances in the field of neuromorphic computing. Analyzing developments of
spiking neural network algorithms and neuromorphic hardware as a co-design game
we use the Stag Hunt model to illustrate challenges for spiking algorithms or
architectures to advance the field independently and advocate for a strategic
pursuit to advance neuromorphic computing.