Why FPGAs Steal the Spotlight in Hardware Acceleration for AI Applications?

Introduction

One of the solutions that has emerged in recent years in the field of AI Hardware is the use of field-programmable gate arrays (FPGAs) for AI acceleration.

FPGAs are devices that can be reconfigured multiple times for different purposes, allowing developers to customize their hardware design to match the specific requirements of their AI workloads.

FPGAs offer several advantages over other types of hardware, such as graphics processing units (GPUs) and application-specific integrated circuits (ASICs), in terms of flexibility, performance, and power efficiency.

What are FPGAs and how do they work?

FPGAs are devices that consist of an array of logic blocks and a way to program the blocks and the relationships among them. Unlike classic chips, FPGAs can be reconfigured multiple times for different purposes, making them a generic tool that can be customized for multiple uses.

To specify the configuration of an FPGA, developers use hardware description languages (HDLs), such as Verilog and VHDL, which describe the functionality and structure of the circuit.

For example, FPGAs can support compact data types, such as 8-bit or 16-bit integers, instead of standard 32-bit floating-point data (FP32), which can reduce the memory bandwidth and power consumption of AI applications without sacrificing accuracy.

FPGAs can also exploit the sparsity and redundancy of AI data and models, which can further improve the efficiency and performance of AI workloads.

An analogy for FPGA-based AI acceleration

Imagine that you are a chef who needs to prepare different dishes for different customers. You have a kitchen with various appliances and utensils, such as ovens, stoves, microwaves, blenders, knives, spoons, etc. How would you use your kitchen to cook the dishes efficiently and effectively?

One way to do it is to use a fixed kitchen layout, where each appliance and utensil has a fixed location and function. For example, you always use the oven to bake, the stove to boil, the microwave to heat, the blender to mix, the knife to cut, and the spoon to stir. This is similar to using a GPU or an ASIC for AI acceleration, where each component has a fixed architecture and operation.

This can be fast and easy, as you don’t need to change anything in your kitchen, and you can use the same recipes and methods for each dish. However, this can also be wasteful and inefficient, as you may not need or use all the appliances and utensils for each dish.

Benefits of using FPGAs for AI

FPGAs have several benefits for AI acceleration, such as:

Flexibility: FPGAs enable customization for various AI workloads like image recognition, natural language processing, speech synthesis, and recommendation systems. Integrating FPGAs with other hardware types, such as CPUs, GPUs, and ASICs, forms heterogeneous systems that actively leverage the strengths of each component. This active integration optimizes performance and efficiency, allowing for a tailored approach to diverse AI tasks.

Performance: FPGAs can achieve high performance for AI workloads by exploiting the parallelism, locality, and pipelining of the algorithms. FPGAs can also optimize the data movement and computation by using custom data types, compression, pruning, quantization, and other techniques.

Power efficiency: FPGAs can reduce the power consumption of AI workloads by using low-power components, such as transistors, capacitors, and resistors, and by minimizing the switching activity and leakage currents of the circuit.

Challenges of using FPGAs for AI

Complexity: FPGAs require deep hardware expertise and knowledge of HDLs, which are not familiar to most software-oriented AI developers. FPGAs also have limited resources, such as memory, logic, and I/O, which can impose constraints on the design and implementation of AI accelerators.

Portability:The lack of standardization across different vendors and models complicates the porting and reuse of designs and codes for various FPGAs. It also have different interfaces and protocols for communication and integration with other devices, which can complicate the deployment and maintenance of AI systems.

FPGAs applications in AI

FPGAs are being used and researched for AI in various ways, such as:

AI inference soft processor overlays:

These are software layers that abstract the FPGA hardware and provide a high-level programming interface for AI developers. Developers leverage AI inference soft processor overlays by writing their AI algorithms in high-level programming languages like C or Python.

These languages are then compiled into instructions, directing the execution on an AI-targeted soft processor implemented on the FPGA.

This approach streamlines the development process, allowing for flexibility in algorithm design while optimizing performance through the FPGA-based execution environment.

Per-workload specialized AI accelerators:

These hardware designs customize and optimize for specific AI workloads, like convolutional neural networks (CNNs), recurrent neural networks (RNNs), or graph neural networks (GNNs). They tailor the hardware to efficiently handle the unique requirements of each workload, enhancing overall performance and efficiency in AI processing.

Per-workload specialized AI accelerators can achieve higher performance and efficiency than generic overlays, as they can exploit the characteristics and features of the target workload, such as data types, operations, sparsity, and parallelism.

Hybrid AI systems: These are systems that combine FPGAs with other types of hardware, such as CPUs, GPUs, and ASICs, to form a heterogeneous architecture that can leverage the strengths and mitigate the weaknesses of each component. Hybrid AI systems can balance the trade-offs between flexibility, performance, and power efficiency.

Future directions and opportunities for FPGA-based AI acceleration

FPGA-based AI acceleration is a promising and active area of research and development, which offers many opportunities and directions for future work, such as:

Improving the usability and productivity of FPGAs for AI developers: To achieve this, developers can develop more user-friendly and intuitive tools and frameworks. These tools aim to simplify and automate the design and implementation of FPGA-based AI accelerators, utilizing high-level synthesis, domain-specific languages, libraries, and compilers.

The active development of such resources empowers users to efficiently harness the capabilities of FPGA-based AI accelerators, fostering a more accessible and streamlined approach to designing and implementing advanced algorithms.

Exploring new applications and domains for FPGA-based AI acceleration: To achieve this, developers actively create more user-friendly and intuitive tools and frameworks. These tools aim to simplify and automate the design and implementation of FPGA-based AI accelerators.

The approach involves developing high-level synthesis, domain-specific languages, libraries, and compilers. By adopting these advancements, the goal is to enhance the accessibility and ease of creating FPGA-based AI accelerators, making the process more streamlined and efficient for developers.

Advancing the state-of-the-art of FPGA-based AI acceleration:Conducting more experiments and benchmarks enables us to compare and contrast the performance and efficiency of FPGA-based AI accelerators with other hardware types, including GPUs and ASICs, as well as with other FPGA-based solutions like overlays and specialized accelerators.

Conclusion

FPGAs are devices that can be reconfigured multiple times for different purposes, allowing developers to customize their hardware design to match the specific requirements of their AI workloads. FPGAs offer several advantages over other types of hardware, such as flexibility, performance, and power efficiency, for AI acceleration.

FPGA-based AI acceleration is a promising and active area of research and development, which offers many opportunities and directions for future work. In this article, we have provided a concise background of FPGA technology and its applications in AI, summarized recent developments and findings related to this topic. Thank you for reading.