Heterogeneous Approximation of DNN HW Accelerators based on Channels Vulnerability

Since Deep Neural Networks (DNNs) gracefully withstands approximation due to its inherent redundancy, Approximate Computing (AxC) can be applied to reduce power consumption and execution time. In the literature, several works adopted the AxC paradigm to DNNs in the form of quantization, precision reduction, pruning, and functional approximation. Despite the promising results demonstrated so far, most of the existing works have applied homogeneous AxC techniques, meaning that the same degree of approximation has been applied to the entire DNN. However, different DNN components (i.e., channels, filters, layers, neurons) have different resiliency levels. This paper presents a framework for applying heterogeneous AxC to DNN hardware accelerators. The framework is based on the identification of channel resilience and applying a tailored degree of approximation per channel. Preliminary results carried out on the LeNet-5 model show that by using the proposed framework it is possible to decrease resource utilization by 65.2% and power consumption by 53.4% at the cost of a marginal drop of accuracy from 98.87% to 98.03%.