Iris: Automatic Generation of Efficient Data Layouts for High Bandwidth Utilization

TL;DR

This paper introduces Iris, a methodology that automatically generates data layouts to optimize bandwidth utilization in heterogeneous computing, reducing manual effort and improving data transfer efficiency.

Contribution

Iris automates data layout design for high bandwidth utilization in accelerators, addressing limitations of HLS tools and custom data types.

Findings

Achieves higher bandwidth utilization than naive or HLS-optimized designs.

Reduces manual effort in designing memory interfaces.

Demonstrates effectiveness across various data types and array sizes.

Abstract

Optimizing data movements is becoming one of the biggest challenges in heterogeneous computing to cope with data deluge and, consequently, big data applications. When creating specialized accelerators, modern high-level synthesis (HLS) tools are increasingly efficient in optimizing the computational aspects, but data transfers have not been adequately improved. To combat this, novel architectures such as High-Bandwidth Memory with wider data busses have been developed so that more data can be transferred in parallel. Designers must tailor their hardware/software interfaces to fully exploit the available bandwidth. HLS tools can automate this process, but the designer must follow strict coding-style rules. If the bus width is not evenly divisible by the data width (e.g., when using custom-precision data types) or if the arrays are not power-of-two length, the HLS-generated accelerator will likely not fully utilize the available bandwidth, demanding even more manual effort from the designer. We propose a methodology to automatically find and implement a data layout that, when streamed between memory and an accelerator, uses a higher percentage of the available bandwidth than a naive or HLS-optimized design. We borrow concepts from multiprocessor scheduling to achieve such high efficiency.