Unleashing the Power of Intel's New FPGA Architecture for AI Acceleration

Unleashing the Power of Intel's New FPGA Architecture for AI Acceleration

Table of Contents

1. Introduction
2. The Promise of FPGA Architecture
3. Intel's AI Focus Stratix 10 NX Device
   1. Martin Langhamer - Senior Principal Engineer at Intel
   2. Overview of FPGA Architecture
4. Spatial Acceleration and the Stratix 10 NX
   1. The Concept of Spatial Acceleration
   2. Teraflops and Operator Access
   3. The Power of Parallelism
5. Comparing FPGA with Other AI Accelerators
   1. CPU: Widely Distributed and Versatile
   2. GPU: The High-Profile Accelerator
   3. Application-Specific Accelerators
   4. Matrix Accelerators: A Focus on Efficiency
6. Understanding the NX Architecture
   1. AI Tensor Blocks and Multipliers
   2. The Versatility of Soft Logic
   3. Configurability and Scalability
7. Harnessing the Power of the FPGA
   1. Mapping RNN Nodes to the FPGA
   2. Extending Multiplier Capabilities
   3. Mixing AI and Non-AI Applications
8. Overview of the NXR
   1. Matrix Multipliers and Cascading Blocks
   2. Virtual Bandwidth Expansion
   3. Preloading and Streaming Data
   4. Accessing Individual Operators
9. Real-Life Test Cases
   1. Overcoming Routing Challenges
   2. Timing Enclosure and Speed Grade
   3. The Flexibility of Reconfiguration
10. Conclusion

Intel's New FPGA Architecture Optimized for AI Acceleration

In the rapidly advancing field of artificial intelligence (AI), the need for efficient and powerful hardware accelerators has become paramount. Intel, a technology leader renowned for its processors and semiconductor chips, has recently introduced a groundbreaking FPGA (Field-Programmable Gate Array) architecture optimized specifically for AI acceleration – the Stratix 10 NX. This spatial accelerator represents a significant leap forward in the realm of FPGA technology, offering unparalleled performance and configurability for AI applications.

Introduction

To guide us through the intricacies of the Stratix 10 NX architecture, we turn to Martin Langhamer, Intel's Senior Principal Engineer and the mastermind behind this innovative design. Langhamer's impressive career trajectory traverses notable companies like AMD, IBM, and Altera (acquired by Intel). With his wealth of experience, Langhamer elucidates the key concepts and features of Intel's latest FPGA architecture, shedding light on the spatial accelerator paradigm and its advantages in AI acceleration.

The Promise of FPGA Architecture

At its core, the FPGA is a hardware device that can be programmed to perform specific functions. Unlike traditional processors, which rely on software to execute tasks, the FPGA enables programming at the hardware level, allowing for greater control and customization. One of the distinguishing features of the Stratix 10 NX is its spatial accelerator design. While every FPGA can be considered a spatial accelerator, the NX takes this concept further by focusing on AI acceleration in the spatial domain.

Intel's AI Focus Stratix 10 NX Device

The Stratix 10 NX boasts an impressive array of capabilities. With a high number of teraflops, these devices deliver remarkable performance that is on par with GPUs and other AI accelerators in the market. However, what sets the NX apart is not just its sheer teraflop numbers but the ability to access all the embedded AI operators in Parallel. This parallel access opens up new possibilities for achieving a high sustained-to-peak performance ratio, a crucial factor in AI workloads.

The architecture of the Stratix 10 NX is designed with arithmetic density in mind. The device comprises a staggering half a million hard embedded AI operators, including multipliers and adders. In addition, there are 700,000 ALMs (Adaptive Logic Modules) or soft logic Lookup table register combinations, representing a remarkable level of arithmetic density. However, the true challenge lies in the ability to effectively utilize and access all these operators.

Spatial Acceleration and the Stratix 10 NX

Spatial acceleration, the core philosophy behind the Stratix 10 NX, allows for precise data movement and access where and when it is needed within the FPGA. Unlike other AI accelerators that rely on mapping software to processor cores or Threads, spatial accelerators program the hardware to move data at a granular level. This approach offers significant advantages in terms of performance, as it leverages the ability to access all operators in parallel.

The concept of spatial acceleration is particularly important for AI workloads, where the accessibility of operators in parallel directly impacts the overall performance. In the case of the Stratix 10 NX, the FPGA houses a vast number of AI tensor blocks, each consisting of multiple multipliers. These blocks can be seamlessly cascaded, resulting in a highly scalable architecture that can address even the most demanding AI applications.

With spatial acceleration, the Stratix 10 NX achieves a remarkable level of arithmetic density compared to other FPGAs. By accessing all the operators simultaneously, the FPGA increases the likelihood of achieving a high sustained-to-peak performance ratio. This advantage, coupled with the built-in multipliers and other arithmetic functionalities, makes the Stratix 10 NX a versatile and powerful spatial accelerator.

Pros:

• Unparalleled performance in AI acceleration
• High arithmetic density
• Ability to access all operators in parallel

Cons:

• Complex routing challenges
• Requires expertise in hardware programming

Comparing FPGA with Other AI Accelerators

To fully appreciate the unique capabilities of the Stratix 10 NX, it is important to compare FPGA architecture with other renowned AI accelerators in the market. The CPU, the most widely distributed AI acceleration platform, offers versatility and widespread adoption. Many CPUs have also incorporated low-precision arithmetic, such as Bfloat16 and Int8, to enhance their AI performance.

The GPU, on the other HAND, is considered the pinnacle of AI acceleration. With an extensive library ecosystem and a large pool of experienced programmers, GPUs have established themselves as the go-to choice for AI tasks. Some GPUs even feature tensor cores, which significantly enhance matrix and vector operations.

Apart from CPUs and GPUs, there is also a growing number of application-specific accelerators in the market. These accelerators are often designed around specific applications and employ a multitude of processor cores for efficient execution. Lastly, matrix accelerators focus on accelerating matrix operations, both for inference and training purposes. These accelerators aim to achieve the highest possible efficiency in matrix processing.

In this landscape, the FPGA stands apart as a unique and highly configurable accelerator. Unlike other accelerators that require software mapping to many threads or cores, the FPGA enables programming of the data flow. This allows for precise data movement and on-the-fly optimization, making FPGAs especially suitable for AI workloads that require parallel access to multiple operators.

Understanding the NX Architecture

The Stratix 10 NX architecture builds upon the strengths of the FPGA, with some key enhancements tailored specifically for AI acceleration. At the heart of the NX device are the AI tensor blocks. Each block houses a matrix of multipliers, with the number varying depending on whether it operates in Int8 or Int4 mode. These ai tensor blocks can be cascaded together seamlessly, creating larger super blocks for even greater processing power.

To further enhance its flexibility, the Stratix 10 NX features soft logic, which enables the integration of elementary functions into the data path. This approach ensures zero overhead when incorporating additional functions, allowing for more efficient execution of AI workloads. The combination of AI tensor blocks and soft logic results in an FPGA with an exceptional level of arithmetic density, surpassing what is typically found in other FPGA architectures.

The configurability of the NX device extends beyond the AI tensor blocks and soft logic. Intel has designed the device in a modular fashion, allowing for the aggregation and cascading of multiple blocks. Additionally, the ability to source and sync data from various locations provides further customization options. This flexibility empowers developers to tailor the NX device to their specific AI workloads and optimize performance.

Pros:

• Highly configurable architecture
• Seamless cascading of AI tensor blocks
• Integration of elementary functions in the data path

Cons:

• Complex design process
• Requires expertise in FPGA programming

Harnessing the Power of the FPGA

One of the defining features of FPGAs, including the Stratix 10 NX, is their ability to be reprogrammed on the fly. This feature allows for versatile deployment of AI and non-AI applications on the same hardware. Unlike dedicated AI accelerators, FPGAs offer flexibility by enabling the configuration of different portions of the device to perform specific tasks.

The Stratix 10 NX excels at accelerating recurrent neural network (RNN) nodes. By mapping RNN nodes directly onto the FPGA, developers can take advantage of the high parallelism and customized data flow. This approach allows for highly efficient execution of RNN models, with the FPGA maximizing the utilization of every available operator.

Building upon this capability, the Stratix 10 NX offers extended multiplier functionalities. By utilizing the embedded fp32 adders or soft logic, developers can construct larger multipliers, such as N16 or fp32. This scalability, coupled with innovative relaxation techniques, results in higher densities of larger-precision arithmetic. Despite the increased arithmetic density, the power consumption remains the same, leading to improved power efficiency.

Additionally, the Stratix 10 NX is not limited to AI applications alone. The FPGA's flexible architecture allows for the deployment of non-AI applications, enabling developers to mix and match different workloads. This versatility ensures that the Stratix 10 NX remains a valuable asset across various domains, offering the best of both worlds—AI acceleration and general-purpose computing.

Overview of the NXR

Within the Stratix 10 NX lies a matrix of multipliers and operators, organized into sectors. Each sector contains memory blocks, soft logic blocks, and AI tensor blocks, which form the fundamental building blocks of the device. Notably, AI tensor blocks dominate the architecture, contributing to the exceptional performance and configurability of the FPGA.

The AI tensor blocks within the Stratix 10 NX are key to achieving high-performance AI acceleration. Comprising a matrix of multipliers, these blocks exhibit a significant degree of parallelism. Depending on the specific configuration, a single block may contain 10 Int8 multipliers or 20 Int4 multipliers. These multipliers are set up to perform dot product calculations, and additionally, fp32 conversion and accumulation can be performed. The versatility of the AI tensor blocks allows them to be seamlessly cascaded together, forming larger tensor blocks to address more demanding AI workloads.

The FPGA's routing capabilities play a vital role in ensuring the efficient use of the AI tensor blocks. However, routing presents complex challenges due to the limited number of wires available to access the multipliers. This limitation necessitates the use of virtual bandwidth expansion techniques to maximize the utilization of the multipliers. By carefully designing the FPGA's architecture and employing intelligent data movement strategies, Intel has overcome these challenges, achieving efficient access to the full multiplier capacity.

Pros:

• Superior routing optimization
• Intelligent data movement strategies
• Seamless cascading of AI tensor blocks

Cons:

• Complex routing constraints
• Limited availability of wires

Real-Life Test Cases

To demonstrate the functionality and real-world performance of the Stratix 10 NX, several test cases have been conducted. These test cases showcase the FPGA's ability to overcome routing challenges and deliver exceptional results.

One of the test cases focuses on a pathological situation, where large buffers are positioned at the top and side of the FPGA, resulting in extensive routing requirements. Despite this challenging Scenario, the Stratix 10 NX succeeds in achieving timing closure at a frequency of 400MHz. Alternatively, by restructuring the design into a systolic structure that remains within sector boundaries, the performance increases even further, reaching an impressive timing enclosure of 520MHz. These figures are based on mid-speed grade devices, and higher speed grades can achieve even greater performance.

In addition to impressive performance, the Stratix 10 NX offers significant flexibility in allocating resources. By utilizing only 90% of the tensor blocks for multiplication operations, the remaining blocks can be used for hyperbolic functions or other computations. This allows developers to fine-tune the FPGA's capabilities according to their specific requirements, maximizing the utilization of resources.

Conclusion

The Stratix 10 NX FPGA architecture represents a major breakthrough in AI acceleration. By introducing the concept of spatial acceleration and utilizing the power of parallelism, Intel has created a versatile and configurable platform that caters to the most demanding AI workloads. With its high arithmetic density, exceptional flexibility, and unprecedented level of configurability, the Stratix 10 NX offers a compelling solution for developers seeking powerful and efficient hardware acceleration for AI applications.

In summary, the Stratix 10 NX reaffirms Intel's commitment to driving innovation in the AI landscape. As the demand for high-performance AI accelerators continues to grow, the Stratix 10 NX is poised to revolutionize the industry, providing developers with an exceptional platform to push the boundaries of artificial intelligence.

Highlights:

• Intel introduces the Stratix 10 NX, a spatial accelerator optimized for AI acceleration.
• The Stratix 10 NX offers unparalleled performance and configurability in the FPGA domain.
• The FPGA architecture enables precise data movement and parallel access to operators.
• Spatial acceleration leverages the potential for high sustained-to-peak performance ratios.
• The Stratix 10 NX combines AI tensor blocks and soft logic for exceptional arithmetic density.
• The FPGA's flexibility allows for the coexistence of AI and non-AI applications.
• Intel overcomes complex routing challenges to fully utilize the FPGA's capabilities.

Frequently Asked Questions (FAQ)

Q: What sets the Stratix 10 NX apart from other FPGA architectures? A: The Stratix 10 NX is designed as a spatial accelerator specifically for AI applications. By focusing on spatial acceleration and parallel access to operators, Intel has created an FPGA architecture that delivers exceptional performance and configurability.

Q: How does the Stratix 10 NX compare to CPUs and GPUs in terms of AI acceleration? A: While CPUs and GPUs are widely deployed AI accelerators, they rely on software mapping to processor cores or threads. In contrast, the Stratix 10 NX utilizes spatial acceleration, which allows for precise data movement and optimized access to operators. This unique approach offers advantages in terms of performance and configurability.

Q: Can the Stratix 10 NX be used for applications other than AI? A: Yes, the Stratix 10 NX is a highly versatile FPGA and can be used for both AI and non-AI applications. Its flexible architecture enables developers to configure different portions of the device according to their specific requirements, making it suitable for a wide range of applications.

Q: How does the Stratix 10 NX overcome routing challenges and optimize resource utilization? A: Intel has developed intelligent data movement strategies and virtual bandwidth expansion techniques to maximize the utilization of multipliers within the Stratix 10 NX. By carefully designing the FPGA's architecture and employing innovative approaches, Intel has overcome routing constraints and achieved efficient access to resources.

Q: What types of AI workloads benefit most from the Stratix 10 NX's spatial accelerator design? A: The Stratix 10 NX is well-suited for AI workloads that require parallel access to multiple operators. This includes applications like neural network inference, machine learning, and deep learning, where the ability to access operators in parallel significantly enhances performance.

Q: Can the Stratix 10 NX be reprogrammed for different applications and configurations? A: Yes, one of the key advantages of FPGAs is their ability to be reprogrammed on the fly. This allows developers to adapt the Stratix 10 NX to different applications and configurations, enabling them to maximize the performance and efficiency of their specific workloads.