ProTEA: A Programmable FPGA Accelerator for Efficient Transformer Encoder Inference

Concepts de base

ProTEA is a runtime programmable FPGA accelerator designed to efficiently execute the computationally intensive multi-head attention and feedforward neural network layers of transformer encoder models.

Résumé

The paper introduces ProTEA, a runtime programmable FPGA accelerator designed to efficiently execute the computationally intensive multi-head attention (MHA) and feedforward neural network (FFN) layers of transformer encoder models.
The key highlights are:

ProTEA's architecture maximizes the utilization of DSP units to achieve high parallelism and low latency. It incorporates separate computation engines for the MHA and FFN layers, each with an array of processing elements.

An efficient tiling strategy is employed to partition the large weight matrices into smaller tiles that can fit within the on-chip memory of the FPGA. This enables the accommodation of large transformer models.

The HLS-based design allows for runtime programmability, where key parameters like the number of attention heads, layers, embedding dimension, and sequence length can be adjusted without the need for hardware re-synthesis.

Experimental results on the Xilinx Alveo U55C FPGA show that ProTEA can achieve a maximum frequency of 200 MHz and outperform state-of-the-art FPGA accelerators by 1.3-2.8x in terms of speed and 1.7-3.46x in terms of GOPS/DSP. It also demonstrates 2.5x and 16x speedups over an NVIDIA Titan XP GPU for certain transformer models.

The runtime programmability and efficient hardware design of ProTEA make it a versatile accelerator capable of hosting a wide range of popular transformer networks without the need for hardware re-synthesis.

Stats

The sequence length is varied from 32 to 128.
The embedding dimension is varied from 512 to 768.
The number of attention heads is varied from 2 to 8.
The number of layers is varied from 4 to 12.

Citations

"ProTEA is a runtime programmable FPGA accelerator designed to efficiently execute the computationally intensive multi-head attention and feedforward neural network layers of transformer encoder models."
"An efficient tiling strategy is employed to partition the large weight matrices into smaller tiles that can fit within the on-chip memory of the FPGA. This enables the accommodation of large transformer models."
"Experimental results on the Xilinx Alveo U55C FPGA show that ProTEA can achieve a maximum frequency of 200 MHz and outperform state-of-the-art FPGA accelerators by 1.3-2.8x in terms of speed and 1.7-3.46x in terms of GOPS/DSP."

Idées clés tirées de

ProTEA: Programmable Transformer Encoder Acceleration on FPGA

by Ehsan Kabir,... à arxiv.org 09-24-2024

https://arxiv.org/pdf/2409.13975.pdf

Questions plus approfondies

How can the design of ProTEA be extended to support both the encoder and decoder layers of transformer models?

To extend the design of ProTEA to support both the encoder and decoder layers of transformer models, several modifications and enhancements can be implemented. First, the architecture must incorporate additional processing modules specifically tailored for the decoder's unique requirements, such as the masked attention mechanism and the encoder-decoder attention layer. This involves creating dedicated computation engines (CEs) for these components, similar to the existing multi-head attention (MHA) and feedforward network (FFN) modules in the encoder.
Second, the runtime programmability feature of ProTEA can be leveraged to allow dynamic configuration of the number of layers, attention heads, and embedding dimensions for both the encoder and decoder. This would enable the accelerator to adapt to various transformer architectures without requiring hardware resynthesis. The existing HLS code can be parameterized further to accommodate the additional complexity introduced by the decoder layers.
Moreover, the tiling strategy employed in the encoder can be adapted for the decoder, ensuring efficient memory utilization and parallel processing. The design should also consider the interdependencies between the encoder and decoder, particularly in how the output from the encoder is utilized in the decoder's attention mechanism. By implementing these changes, ProTEA can effectively support the full transformer architecture, enhancing its versatility and performance across a broader range of applications.

What are the potential trade-offs between the flexibility offered by runtime programmability and the performance gains achieved by custom hardware designs?

The flexibility offered by runtime programmability in ProTEA allows for dynamic adjustments to various parameters, such as the number of attention heads, layers, and embedding dimensions, without the need for hardware resynthesis. This adaptability is particularly beneficial for applications requiring different transformer configurations, as it reduces development time and allows for rapid prototyping and testing of various models.
However, this flexibility may come at the cost of performance. Custom hardware designs, which are specifically tailored for a particular model or application, can achieve higher efficiency and lower latency due to their optimized architecture. These designs can exploit the full capabilities of the FPGA, such as maximizing DSP utilization and minimizing memory access times, leading to significant performance gains.
In contrast, a runtime-programmable design may introduce overhead due to the need for additional control logic and the potential for suboptimal resource allocation. The trade-off lies in balancing the need for flexibility against the desire for peak performance. While ProTEA's design allows for a wide range of applications, it may not achieve the same level of optimization as a custom-designed accelerator focused on a specific transformer model. Therefore, the choice between flexibility and performance will depend on the specific use case and the importance of adaptability versus efficiency in the target application.

How can the tiling strategy in ProTEA be further optimized to improve the utilization of on-chip memory and computing resources on the FPGA?

To further optimize the tiling strategy in ProTEA for improved utilization of on-chip memory and computing resources, several approaches can be considered. First, a more granular tiling approach could be implemented, where the weight matrices and input data are partitioned into smaller tiles based on the specific characteristics of the transformer model being executed. This would allow for better fitting of the data into the limited on-chip memory, reducing the need for off-chip memory accesses and improving overall latency.
Second, dynamic tiling could be introduced, where the tile sizes are adjusted at runtime based on the current workload and available resources. This adaptability would enable ProTEA to optimize memory usage and computational efficiency based on the specific demands of the transformer model being processed, allowing for better resource allocation and minimizing idle time for DSPs.
Additionally, the tiling strategy could be enhanced by incorporating data locality principles, ensuring that data required for computations is stored close to the processing elements (PEs). This can be achieved by organizing the data in a way that maximizes the use of local memory (BRAMs) and minimizes data transfer times, which are critical for maintaining high throughput.
Lastly, exploring advanced memory management techniques, such as double buffering or pipelining data loads and computations, can further enhance the efficiency of the tiling strategy. By overlapping data fetching with computation, ProTEA can reduce idle cycles and improve the overall throughput of the accelerator. These optimizations would collectively enhance the performance of ProTEA, making it more effective in handling large transformer models while maximizing the utilization of FPGA resources.