  Complex Deep Neural Networks such as Capsule Networks (CapsNets) exhibit high
learning capabilities at the cost of compute-intensive operations. To enable
their deployment on edge devices, we propose to leverage approximate computing
for designing approximate variants of the complex operations like softmax and
squash. In our experiments, we evaluate tradeoffs between area, power
consumption, and critical path delay of the designs implemented with the ASIC
design flow, and the accuracy of the quantized CapsNets, compared to the exact
functions.
