SYprox: Combining Host and Device Perforation with Mixed Precision Approximation on Heterogeneous Architectures

Approximate computing is an emerging paradigm that aims to exploit the inherent error tolerance of many applications, particularly in domains such as image processing and machine learning. Taking advantage of this property, applications can trade off accuracy for significant gains in performance and power consumption. Existing approximation techniques for GPUs are limited to very specific approaches, do not fully exploit the host-device execution model, and are often restricted in terms of programming models and supported target hardware. This paper introduces SYprox, a new approximate computing framework based on SYCL that allows programmers to easily implement heterogeneous approximated applications. SYprox supports multiple techniques, including data perforation, signal reconstruction, and mixed precision, and allows them to be combined to support a wide range of approximations. In particular, SYprox extends existing perforation approaches to allow both host and device data perforation. Experimental results show that SYprox's approximations are Pareto dominant with respect to state-of-the-art approaches and are portable to AMD, Intel and NVIDIA GPUs.