  Approximate computing (AxC) has been long accepted as a design alternative
for efficient system implementation at the cost of relaxed accuracy
requirements. Despite the AxC research activities in various application
domains, AxC thrived the past decade when it was applied in Machine Learning
(ML). The by definition approximate notion of ML models but also the increased
computational overheads associated with ML applications-that were effectively
mitigated by corresponding approximations-led to a perfect matching and a
fruitful synergy. AxC for AI/ML has transcended beyond academic prototypes. In
this work, we enlighten the synergistic nature of AxC and ML and elucidate the
impact of AxC in designing efficient ML systems. To that end, we present an
overview and taxonomy of AxC for ML and use two descriptive application
scenarios to demonstrate how AxC boosts the efficiency of ML systems.
