Deep Learning on FPGAs: Past, Present, and Future (1602.04283v1)

Abstract: The rapid growth of data size and accessibility in recent years has instigated a shift of philosophy in algorithm design for artificial intelligence. Instead of engineering algorithms by hand, the ability to learn composable systems automatically from massive amounts of data has led to ground-breaking performance in important domains such as computer vision, speech recognition, and natural language processing. The most popular class of techniques used in these domains is called deep learning, and is seeing significant attention from industry. However, these models require incredible amounts of data and compute power to train, and are limited by the need for better hardware acceleration to accommodate scaling beyond current data and model sizes. While the current solution has been to use clusters of graphics processing units (GPU) as general purpose processors (GPGPU), the use of field programmable gate arrays (FPGA) provide an interesting alternative. Current trends in design tools for FPGAs have made them more compatible with the high-level software practices typically practiced in the deep learning community, making FPGAs more accessible to those who build and deploy models. Since FPGA architectures are flexible, this could also allow researchers the ability to explore model-level optimizations beyond what is possible on fixed architectures such as GPUs. As well, FPGAs tend to provide high performance per watt of power consumption, which is of particular importance for application scientists interested in large scale server-based deployment or resource-limited embedded applications. This review takes a look at deep learning and FPGAs from a hardware acceleration perspective, identifying trends and innovations that make these technologies a natural fit, and motivates a discussion on how FPGAs may best serve the needs of the deep learning community moving forward.

• The paper demonstrates that FPGA flexibility allows for custom deep learning architectures that can outperform traditional GPUs in energy efficiency and performance.
• The paper introduces advanced high-level synthesis and OpenCL tools that simplify FPGA adoption, reducing the steep learning curve of traditional design methods.
• The paper forecasts that enhanced memory capacities and interconnects will drive FPGA integration in scalable, energy-sensitive AI deployments.

Deep Learning on FPGAs: Past, Present, and Future

The research paper titled "Deep Learning on FPGAs: Past, Present, and Future" explores the utilization of Field Programmable Gate Arrays (FPGAs) as an alternative hardware acceleration platform for deep learning applications, traditionally dominated by GPUs. This paper offers a comprehensive evaluation of the historical and contemporary landscape of FPGAs within deep learning, emphasizing the potential avenues for integrating these technologies to support the increasing demands for computational power and scalability in artificial intelligence.

FPGAs provide a compelling alternative to GPUs due to their flexibility in configurability, which allows for tailored architectures that can be optimized for the specific characteristics of deep learning algorithms. This architectural flexibility provides an opportunity for model-level optimizations that are not feasible in fixed architecture systems like GPUs. Additionally, FPGAs tend to yield higher performance per watt, making them particularly useful in energy-sensitive applications, such as resource-constrained embedded systems or large-scale server deployments.

The paper underscores several characteristics of deep learning models—data parallelism, model parallelism, and pipeline parallelism—highlighting how these features naturally align with the FPGA architecture. By configuring FPGA hardware to suit specific applications, designers can develop personalized circuits that surpass the performance boundaries set by GPUs, which rely on fixed architectures that necessitate algorithmic adaptation to their parallel processing models.

Despite the potential FPGAs hold, their adoption has been historically impeded by the steep learning curve associated with their design tools, primarily based on hardware description languages such as Verilog and VHDL. However, recent advancements in high-level synthesis tools and the adoption of standard parallel programming frameworks like OpenCL have significantly lowered the barriers to entry, aligning FPGA design experiences closer to mainstream software development practices.

Looking into the future, FPGAs present promising solutions for several challenges associated with the scalability of deep learning. The growing complexity and size of datasets demand flexible, scalable solutions, and the FPGA market continues to evolve to support larger memory capacities, smaller feature sizes, and improved interconnects. The acquisition of Altera by Intel and partnerships such as IBM and Xilinx suggest an increased integration of FPGA technology in data center applications.

Further exploration of FPGA compatibility with popular deep learning software is a critical avenue for research and development. While deep learning frameworks like Caffe, Torch, and Theano are starting to offer OpenCL support, there's still a substantial opportunity for developing explicit support for FPGAs within these tools, facilitated by high-level abstraction methods.

In conclusion, FPGAs offer a strategic advantage in powering deep learning applications, with the potential to redefine architectural norms by facilitating greater exploratory freedom for deep learning researchers. As the need for efficient data processing solutions grows, FPGAs will play an essential role in enabling modern AI applications to achieve unprecedented performance, providing a tailored approach to hardware acceleration that addresses both current and future deep learning needs.