ImaGen: A General Framework for Generating Memory- and Power-Efficient Image Processing Accelerators

Image processing algorithms are prime targets for hardware acceleration as they are commonly used in resource-and power-limited applications. Today's image processing accelerator designs make rigid assumptions about the algorithm structures and/or on-chip memory resources. As a result, they either have narrow applicability or result in inefficient designs.

This paper presents a compiler framework that automatically generates memory-and power-efficient image processing accelerators. We allow programmers to describe generic image processing algorithms (in a domain specific language) and specify on-chip memory structures available. Our framework then formulates a constrained optimization problem that minimizes on-chip memory usage while maintaining theoretical maximum throughput. The key challenge we address is to analytically express the throughput bottleneck, on-chip memory contention, to enable a lightweight compilation. FPGA prototyping and ASIC synthesis show that, compared to existing approaches, accelerators generated by our framework reduce the on-chip memory usage and/or power consumption by double digits. ImaGen code is available at: https://github.com/horizon-research/imagen.