Field-Programmable Gate Array Architecture for Deep Learning: Survey & Future Directions (2404.10076v1)

Abstract: Deep learning (DL) is becoming the cornerstone of numerous applications both in datacenters and at the edge. Specialized hardware is often necessary to meet the performance requirements of state-of-the-art DL models, but the rapid pace of change in DL models and the wide variety of systems integrating DL make it impossible to create custom computer chips for all but the largest markets. Field-programmable gate arrays (FPGAs) present a unique blend of reprogrammability and direct hardware execution that make them suitable for accelerating DL inference. They offer the ability to customize processing pipelines and memory hierarchies to achieve lower latency and higher energy efficiency compared to general-purpose CPUs and GPUs, at a fraction of the development time and cost of custom chips. Their diverse high-speed IOs also enable directly interfacing the FPGA to the network and/or a variety of external sensors, making them suitable for both datacenter and edge use cases. As DL has become an ever more important workload, FPGA architectures are evolving to enable higher DL performance. In this article, we survey both academic and industrial FPGA architecture enhancements for DL. First, we give a brief introduction on the basics of FPGA architecture and how its components lead to strengths and weaknesses for DL applications. Next, we discuss different styles of DL inference accelerators on FPGA, ranging from model-specific dataflow styles to software-programmable overlay styles. We survey DL-specific enhancements to traditional FPGA building blocks such as logic blocks, arithmetic circuitry, and on-chip memories, as well as new in-fabric DL-specialized blocks for accelerating tensor computations. Finally, we discuss hybrid devices that combine processors and coarse-grained accelerator blocks with FPGA-like interconnect and networks-on-chip, and highlight promising future research directions.

FPGA Architecture for Deep Learning Acceleration

The paper "Field-Programmable Gate Array Architecture for Deep Learning: Survey and Future Directions" offers a comprehensive analysis of the evolving role of FPGAs in the domain of deep learning (DL) acceleration. Recognizing the increasing computational demands of DL workloads, the authors delve into how FPGAs can fulfill these requirements with their unique blend of flexibility, performance, and adaptability.

Summary of FPGA Advantages for DL

FPGA devices possess several intrinsic strengths that make them suitable for DL tasks:

1. Custom Precision and Dataflow: FPGAs allow for the implementation of low-precision arithmetic operations which are often suitable for DL inference, leading to area and power savings. This specificity is in contrast to CPUs and GPUs which adhere to fixed precision formats.
2. Spatial Architecture: The spatial nature of FPGAs enables direct data flow between computing elements, reducing latency significantly and enhancing performance for applications with tight latency constraints.
3. Reconfigurability: The ability to reconstruct the FPGA for specific DL models offers an edge over ASICs by adapting to newly developed models and facilitating rapid deployment.
4. Diverse IO Capabilities: FPGAs support a variety of interfaces, allowing them to be integrated with different sensors and peripherals which is advantageous for edge DL applications.

Design Styles for DL Acceleration

The paper explores various design methodologies for implementing DL accelerators on FPGAs:

• Custom Hardware Generation: This approach automatically generates model-specific hardware, optimizing resources, and is demonstrated by tools like HPIPE. Such tools enable bespoke pipeline architectures tailored to individual models, offering performance improvements but requiring longer synthesis times.
• FPGA Overlays: These software-programmable architectures such as the NPU overlay deliver high performance for batch-1 inference tasks by abstracting hardware details and enabling flexible deployment across multiple DL workloads.

FPGA Architecture Enhancements

Several architecture modifications have been researched and proposed to optimize FPGAs for DL:

1. Logic Blocks: Enhancements in logic block design can increase the density of low-precision arithmetic operations, a critical requirement for efficient DL inference.
2. DSP Blocks: Augmenting digital signal processing blocks with capabilities for lower precision operations can significantly improve multiplication and accumulation throughput.
3. Block RAMs (BRAMs): By integrating compute capabilities within BRAMs, data movement can be minimized, conserving power and routing resources.
4. Interposer Technology: Advanced packaging techniques enable integration of multiple dice, crucial for constructing larger, more capable FPGA systems for DL.
5. Networks-on-Chip and AI Engines: Emerging architectures like AMD’s Versal incorporate AI engines connected by a network-on-chip (NoC), suiting them for a wide range of DL applications by combining FPGA flexibility with efficient coarse-grained accelerators.

Implications and Future Directions

The path forward suggests several promising avenues for enhancing FPGAs in DL contexts. The paper highlights potential improvements including deeper integration of AI-specific hard blocks and exploration of new design paradigms that mix reconfigurable logic with fixed-function ASIC-like elements. Future architectures may leverage 2D/3D integration to further improve performance and energy efficiency.

In summary, the paper underscores that with strategic architectural innovations, FPGAs hold substantial promise for efficiently accelerating DL workloads, spanning from large-scale datacenter applications to resource-constrained edge environments.