  Machine learning, particularly deep neural network inference, has become a
vital workload for many computing systems, from data centers and HPC systems to
edge-based computing. As advances in sparsity have helped improve the
efficiency of AI acceleration, there is a continued need for improved system
efficiency for both high-performance and system-level acceleration.
  This work takes a unique look at sparsity with an event (or
activation-driven) approach to ANN acceleration that aims to minimize useless
work, improve utilization, and increase performance and energy efficiency. Our
analytical and experimental results show that this event-driven solution
presents a new direction to enable highly efficient AI inference for both CNN
and MLP workloads.
  This work demonstrates state-of-the-art energy efficiency and performance
centring on activation-based sparsity and a highly-parallel dataflow method
that improves the overall functional unit utilization (at 30 fps). This work
enhances energy efficiency over a state-of-the-art solution by 1.46$\times$.
Taken together, this methodology presents a novel, new direction to achieve
high-efficiency, high-performance designs for next-generation AI acceleration
platforms.
