Exploiting Functional Approximation on Decision-Tree Based Multiple Classifier Systems

Multiple Classifier Systems (MCSs) have been in-creasingly designed to take advantage of hardware features, such as high parallelism and computational power, to guarantee higher throughput and lower latency. Although the combination of multiple classifiers leads to high classification accuracy, the required area overhead makes the design of a hardware accelerator unfeasible, hindering the adoption of commercial configurable devices. For this reason, in this paper, we exploit the Approximate Computing (AxC) design paradigm to automatically generate approximated hardware implementations of MCSs by trading hardware area overhead off for classification accuracy. In particular, we propose an algorithm that identifies the resiliency source of the model and uses it to introduce approximation with minimum accuracy loss.