  Recently ConvNets or convolutional neural networks (CNN) have come up as
state-of-the-art classification and detection algorithms, achieving near-human
performance in visual detection. However, ConvNet algorithms are typically very
computation and memory intensive. In order to be able to embed ConvNet-based
classification into wearable platforms and embedded systems such as smartphones
or ubiquitous electronics for the internet-of-things, their energy consumption
should be reduced drastically. This paper proposes methods based on approximate
computing to reduce energy consumption in state-of-the-art ConvNet
accelerators. By combining techniques both at the system- and circuit level, we
can gain energy in the systems arithmetic: up to 30x without losing
classification accuracy and more than 100x at 99% classification accuracy,
compared to the commonly used 16-bit fixed point number format.
