  Augmented Reality/Virtual Reality (AR/VR) glasses are widely foreseen as the
next generation computing platform. AR/VR glasses are a complex "system of
systems" which must satisfy stringent form factor, computing-, power- and
thermal- requirements. In this paper, we will show that a novel distributed
on-sensor compute architecture, coupled with new semiconductor technologies
(such as dense 3D-IC interconnects and Spin-Transfer Torque Magneto Random
Access Memory, STT-MRAM) and, most importantly, a full hardware-software
co-optimization are the solutions to achieve attractive and socially acceptable
AR/VR glasses. To this end, we developed a semi-analytical simulation framework
to estimate the power consumption of novel AR/VR distributed on-sensor
computing architectures. The model allows the optimization of the main
technological features of the system modules, as well as the computer-vision
algorithm partition strategy across the distributed compute architecture. We
show that, in the case of the compute-intensive machine learning based Hand
Tracking algorithm, the distributed on-sensor compute architecture can reduce
the system power consumption compared to a centralized system, with the
additional benefits in terms of latency and privacy.
