The posit representation for real numbers is an alternative to the ubiquitous IEEE 754
floating-point standard. In this work, we present PERCIVAL, an application-level posit RISC …

The Posit™ Number System was introduced in 2017 as a replacement for floating-point
numbers. Since then, the community has explored its application in several areas, such as …

Traditional Deep Neural Network (DNN) quantization methods using integer or floating-point
data types struggle to capture diverse DNN parameter distributions and often require large …

Posit arithmetic has caught the attention of the research community as one of the most
promising alternatives to the IEEE 754 standard for floating-point arithmetic. However, the …

Low-precision formats have proven to be an efficient way to reduce not only the memory
footprint but also the hardware resources and power consumption of deep learning …

Posit arithmetic is an alternative format to the standard IEEE 754 for floating-point numbers
that claims to provide compelling advantages over floats, including higher accuracy, larger …

The high computational complexity, memory footprints, and energy requirements of machine
learning models, such as Artificial Neural Networks (ANNs), hinder their deployment on …

Growing constraints on memory utilization, power consumption, and I/O throughput have
increasingly become limiting factors to the advancement of high performance computing …

In this paper, a new adaptive quantization algorithm for generalized posit format is
presented, to optimally represent the dynamic range and distribution of deep neural network …

Many applications require high-precision arithmetic. IEEE 754-2019 compliant (floating-
point) arithmetic is the de facto standard for performing these computations. Recently, posit …