Programming Heterogeneous Systems from an Image Processing DSL (1610.09405v1)

Abstract: Specialized image processing accelerators are necessary to deliver the performance and energy efficiency required by important applications in computer vision, computational photography, and augmented reality. But creating, "programming,"and integrating this hardware into a hardware/software system is difficult. We address this problem by extending the image processing language, Halide, so users can specify which portions of their applications should become hardware accelerators, and then we provide a compiler that uses this code to automatically create the accelerator along with the "glue" code needed for the user's application to access this hardware. Starting with Halide not only provides a very high-level functional description of the hardware, but also allows our compiler to generate the complete software program including the sequential part of the workload, which accesses the hardware for acceleration. Our system also provides high-level semantics to explore different mappings of applications to a heterogeneous system, with the added flexibility of being able to map at various throughput rates. We demonstrate our approach by mapping applications to a Xilinx Zynq system. Using its FPGA with two low-power ARM cores, our design achieves up to 6x higher performance and 8x lower energy compared to the quad-core ARM CPU on an NVIDIA Tegra K1, and 3.5x higher performance with 12x lower energy compared to the K1's 192-core GPU.

Overview of "Programming Heterogeneous Systems from an Image Processing DSL"

The paper "Programming Heterogeneous Systems from an Image Processing DSL" presents an innovative approach to streamline the development and integration of hardware accelerators for image processing applications. The authors propose enhancements to the domain-specific language (DSL) Halide, making it instrumental in generating both hardware accelerators and the corresponding software "glue" code for seamless CPU and FPGA interaction. This development is positioned to address the increasing demand for performance and energy efficiency in image processing tasks driven by fields such as computer vision, computational photography, and augmented reality.

Key Contributions

The authors highlight three primary advancements in the synthesis of Halide DSL for hardware generation:

1. Extension of Halide for Hardware Generation: By creatively extending Halide with minimal additions, the authors enable the DSL to specify sections of code that should be hardware accelerated. Leveraging Halide's scheduling prowess, the system overcomes the challenge of hardware realization, balancing software workloads on CPUs and hardware capabilities on FPGAs. This not only broadens the scope of applications amenable to acceleration but also maintains the high-level functional abstraction that Halide provides.
2. Refined Dataflow Hardware Architecture: The paper adapts and augments the traditional line-buffered pipeline architecture to generate flexible hardware implementations from Halide DSL descriptions. Improvements include handling higher-dimension data and affine indices in computations, offering a wider architectural template that captures the diversity of Halide applications.
3. Comprehensive End-to-End System: The approach delivers a complete development chain that compiles Halide specifications into FPGA bitstreams, accompanying them with multi-threaded software and driver components. This feature eases the partitioning of workloads between CPU and FPGA, essential for optimizing system-level performance.

Performance and Efficiency

The authors validate their method by mapping several image processing applications, including Gaussian filtering and stereo depth computation, onto a Xilinx Zynq platform, which combines ARM cores and FPGA fabric. The results indicate substantial improvements, displaying up to sixfold increase in performance and a 38-fold decrease in energy consumption compared to conventional CPU-based implementations on similar technology nodes. These efficiencies underscore the importance of locality in data handling and the tailored execution flow enabled by the DSL.

Implications and Future Directions

This paper underscores the potential for DSLs like Halide to act as bridges between high-level algorithm specification and low-level hardware execution, providing a simplification path for developers less versed in hardware intricacies. The system not only mitigates the complexity of hardware synthesis but also demonstrates the feasibility and benefits of tightly-coupled software-hardware codesign.

The work presented forms a foundation for several future research avenues. Enhanced automation for scheduling and data buffering can augment the tool's utility. Moreover, the principles elucidated could be generalized for other specialized processors or configurable architectures, offering further insights into efficient programmable image signal processors (ISPs). As application demands grow increasingly complex, such frameworks may become invaluable in developing robust and efficient solutions in real-time and power-constrained environments.

In conclusion, "Programming Heterogeneous Systems from an Image Processing DSL" advances the discipline by merging established high-level programming paradigms with low-level execution efficiency, fostering more sophisticated, capable, and energy-aware imaging systems.