sensAI: ConvNets Decomposition via Class Parallelism for Fast Inference on Live Data

Convolutional Neural Networks (ConvNets) enable computers to excel on vision learning tasks such as image classification, object detection. Recently, real-time inference on live data is becoming more and more important. From a system perspective, it requires fast inference on each single, incoming data item (e.g. 1 image). Two main-stream distributed model serving paradigms – data parallelism and model parallelism – are not necessarily desirable here, because we cannot further split a single input data piece via data parallelism, and model parallelism introduces huge communication overhead. To achieve live data inference with low latency, we propose sensAI, a novel and generic approach that decouples a CNN model into disconnected subnets, each is responsible for predicting certain class(es). We call this new model distribution paradigm as class parallelism. Experimental results show that, sensAI achieves up to 18x faster inference on single input data item with no or negligible accuracy loss on CIFAR-10, CIFAR-100 and ImageNet-1K datasets.