Voice-activity-detection (VAD) is the task of predicting where in the utterance is speech versus background noise. It is an important first step to determine when to open the microphone (i.e., start-of- speech) and close the microphone (i.e., end-of-speech) for streaming speech recognition applications such as Voice Search. Long short- term memory neural networks (LSTMs) have been a popular archi- tecture for sequential modeling for acoustic signals, and have been successfully used for many VAD applications. However, it has been observed that LSTMs suffer from state saturation problems when the utterance is long (i.e., for voice dictation tasks), and thus requires the LSTM state to be periodically reset. In this paper, we propse an alter- native architecture that does not suffer from saturation problems by modeling temporal variations through a stateless dilated convolution neural network (CNN). The proposed architecture differs from con- ventional CNNs in three respects (1) dilated causal convolution, (2) gated activations and (3) residual connections. Results on a Google Voice Typing task shows that the proposed architecture achieves 14% rela- tive FA improvement at a FR of 1% over state-of-the-art LSTMs for VAD task. We also include detailed experiments investigating the factors that distinguish the proposed architecture from conventional convolution.