The Universal library contains parameterized number systems to aid in the research to create custom algorithms that are tailored to the arithmetic requirements of the applications. In high-performance and real-time applications, this tailored number system approach has been a common approach to maximize performance and/or performance per Watt. More recently, Deep Learning has rekindled the interest in tailored number systems as training DNNs is a lengthy affair and improving computational performance by minimizing hardware, memory or network bandwidth, had immediate commercial benefits. As Deep Learning AI is getting embedded in stand-alone devices, the benefit of multi-precision number system optimizations is broadening to any application that uses deep computes to deliver knowledge generation or decision making.