Energy-Driven Precision Scaling for Fixed-Point ConvNets

Data precision scaling is a well-known technique for power/energy minimization in error-resilient applications. It has proven particularly suited for embedded Convolutional Neural Networks (ConvNets) made run on fixed-point arithmetic coprocessors. The key observation is that methods that only account for accuracy during the precision assignment process may lead to sub-optimal energy minimization. This work introduces an energy-driven optimization that delivers per-layer quantization under a user-defined accuracy constraint. The tool is conceived for accelerators that dynamically adapt their energy and accuracy through software-programmable multiprecision Multiply&Accumulate (MAC) units. Simulation results collected on different ConvNets trained with public data-set show substantial energy savings and improved energy-accuracy tradeoffs w.r.t. conventional fixed-point methods.