Spiking neural network (SNN) is an event-driven neural network that can greatly reduce the power consumption of the conventional artificial neural networks (ANN). Many ANN models can be converted to SNN models when the activation function is ReLU. For ANN models with other activation functions, such as the Leaky ReLU function, the converted SNN models either suffer from serious accuracy degradation or require a long time step. In this paper, we propose a fast and accurate ANN-SNN conversion algorithm for models with the Leaky ReLU function. We design a novel neuron model that supports negative spikes. To address the problem of long tail distribution in the activation values, we propose a threshold optimization algorithm based on the variance of the activation values. To avoid the problem of error accumulation, we jointly calibrate all layers in the SNN model with adaptive weighting. Experiment results verify the effectiveness of the proposed algorithm.

This paper aims to develop flexible and scalable scene understanding capabilities usable on resource-constrained devices. We propose an edge computing framework to enable large-scale object detection within complex visual scenes. The framework attempts to emulate human intuition - leveraging commonsense reasoning and fast concept learning from small data. Structured knowledge about objects and their relationships guides efficient deduction of scene contents. Specifically, we develop an ontology-based scene deduction model to refine an initial scene parsing, which incorporates declarative constraints on objects, their attributes, and interactions. It represents such domain knowledge explicitly and reasons over ontological relationships. By representing this domain knowledge explicitly and reasoning over ontological relationships, the model refines an initial scene parsing to better reflect commonsense consistency. The evaluation compared the proposed method to other large-scale object detection methods. The results show that our ontology-driven deduction model scale-ups the data-driven scene parser’s capability while reducing the data and compute requirements.