Spike-Based Anytime Perception

In many emerging computer vision applications, it is critical to adhere to stringent latency and power constraints. The current neural network paradigm of frame-based, floating-point inference is often ill-suited to these resource-constrained applications. Spike-based perception - enabled by spiking neural networks (SNNs) - is one promising alternative. Unlike conventional neural networks (ANNs), spiking networks exhibit smooth tradeoffs between latency, power, and accuracy. SNNs are the archetype of an "anytime algorithm" whose accuracy improves smoothly over time. This property allows SNNs to adapt their computational investment in response to changing resource constraints. Unfortunately, mainstream algorithms for training SNNs (i.e., those based on ANN-to-SNN conversion) tend to produce models that are inefficient in practice. To mitigate this problem, we propose a set of principled optimizations that reduce latency and power consumption by 1-2 orders of magnitude in converted SNNs. These optimizations leverage a set of novel efficiency metrics designed for anytime algorithms. We also develop a state-of-the-art simulator, SaRNN, which can simulate SNNs using commodity GPU hardware and neuromorphic platforms. We hope that the proposed optimizations, metrics, and tools will facilitate the future development of spike-based vision systems.