Scalable-Effort ConvNets for Multilevel Classification

This work introduces the concept of scalable-effort Convolutional Neural Networks (ConvNets), an effort-accuracy scalable model for classification of data at multilevel abstraction. Scalable-effort ConvNets are able to adapt at run-time to the complexity of the classification problem, i.e. the level of abstraction defined by the application (or context), and reach a given classification accuracy with minimal computational effort. The mechanism is implemented using a single-weight scalable-precision model rather than an ensemble of quantized weight models; this makes the proposed strategy highly flexible and particularly suited for embedded architectures with limited resource availability. The paper describes (i) a hardware/software vertical implementation of scalable-precision multiply&accumulate arithmetic, (ii) an accuracy-constrained heuristic that delivers near-optimal layer-by-layer precision mapping at a predefined level of abstraction. It also reports the validation for three state-of-the-art nets, i.e. AlexNet, SqueezeNet and MobileNet, trained and tested with ImageNet. Collected results show scalable-effort ConvNets guarantee flexibility and substantial savings: 47.07% computational effort reduction at minimum accuracy, or 30.6% accuracy improvement at maximum effort w.r.t. standard flat ConvNets (average over the three benchmarks for high-level classification).