SNE: an Energy-Proportional Digital Accelerator for Sparse Event-Based Convolutions
Alfio Di Mauro1, Arpan Suravi Prasad1, Zhikai Huang1, Matteo Spallanzani1, Francesco Conti2 and Luca Benini1,2
1Dept. of Information Technology and Electrical Engineering, ETH Zürich, Switzerland
2Dept. of Electrical, Electronic and Information Engineering, University of Bologna, Italy
ABSTRACT
Event-based sensors are drawing increasing attention due to their high temporal resolution, low power consumption, 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.
Keywords: Event-Based Computing, Neuromorphic Platform, Edge-Computing.
