SNE: an Energy-Proportional Digital Accelerator for Sparse Event-Based Convolutions

Event-based sensors are drawing increasing atten-
tion due to their high temporal resolution, low power con-
sumption, and low bandwidth. To efficiently extract semantically
meaningful information from sparse data streams produced by
such sensors, we present a 4.5TOP/s/W digital accelerator capable
of performing 4-bits-quantized event-based convolutional neural
networks (eCNN). Compared to standard convolutional engines,
our accelerator performs a number of operations proportional
to the number of events contained into the input data stream,
ultimately achieving a high energy-to-information processing
proportionality. On the IBM-DVS-Gesture dataset, we report
80uJ/inf to 261uJ/inf, respectively, when the input activity is 1.2%
and 4.9%. Our accelerator consumes 0.221pJ/SOP, to the best of
our knowledge it is the lowest energy/OP reported on a digital
neuromorphic engine.