Prediction of Inference Energy on CNN Accelerators Supporting Approximate Circuits

• https://ieeexplore.ieee.org/document/10139724

approximate computing, prediction, hardware accelerator, convolutional neural
networks

Design methodologies developed for optimizing hardware implementations of
convolutional neural networks (CNN) or searching for new hardware-aware neural
architectures rely on the fast and reliable estimation of key hardware
parameters, such as the energy needed for one inference. Utilizing approximate
circuits in hardware accelerators of CNNs faces the designers with new problems
during their simulation - commonly used tools (TimeLoop, Accelergy, Maestro) do
not support approximate arithmetic operations. This work addresses the fast and
efficient prediction of consumed energy in hardware accelerators of CNNs that
utilize approximate circuits such as approximate multipliers. First, we extend
the state-of-the-art software frameworks TimeLoop and Accelergy to predict the
inference energy when exact multipliers are replaced with various approximate
implementations. The energies obtained using the modified tools are then
considered the ground truth (reference) values. Then, we propose and evaluate,
using two accelerators (Eyeriss and Simba) and two types of networks (CNNs
generated by EvoApproxNAS and standard ResNet CNNs), two predictors of inference
energy. We conclude that a simple predictor based on summing the energies needed
for all multiplications highly correlates with the reference values if the CNN's
architecture is fixed. For complex CNNs with variable architectures typically
generated by neural architecture search algorithms, a more sophisticated
predictor based on a machine learning model has to be employed. The proposed
predictors are 420-533× faster than reference solutions.