Memristor-based perceptron implementation has recently attracted increased interest. Previous perceptron demonstrations perform some operations using software, leading to back-and-forth communication between the perceptron and a computer. In this work, we show that by implementing the activation functions all on hardware, we can avoid those unnecessary communication and improve power and throughput. We have designed a compact multi-channel rectified linear unit activation function and developed a two-layer perceptron using two memristor arrays. We have achieved a 93.63% recognition accuracy in classifying the MNIST dataset using the analog neurons, comparable with that for a partially software counterpart, and a much-improved power efficiency.