Hardware Acceleration of LLMs: A comprehensive survey and comparison (2409.03384v1)

Abstract: LLMs have emerged as powerful tools for natural language processing tasks, revolutionizing the field with their ability to understand and generate human-like text. In this paper, we present a comprehensive survey of the several research efforts that have been presented for the acceleration of transformer networks for LLMs using hardware accelerators. The survey presents the frameworks that have been proposed and then performs a qualitative and quantitative comparison regarding the technology, the processing platform (FPGA, ASIC, In-Memory, GPU), the speedup, the energy efficiency, the performance (GOPs), and the energy efficiency (GOPs/W) of each framework. The main challenge in comparison is that every proposed scheme is implemented on a different process technology making hard a fair comparison. The main contribution of this paper is that we extrapolate the results of the performance and the energy efficiency on the same technology to make a fair comparison; one theoretical and one more practical. We implement part of the LLMs on several FPGA chips to extrapolate the results to the same process technology and then we make a fair comparison of the performance.

Hardware Acceleration of LLMs: A Comprehensive Survey and Comparison

Hardware Acceleration of LLMs: A Comprehensive Survey and Comparison authored by Nikoletta Koilia and Christoforos Kachris explores the critical field of enhancing the performance of LLMs using hardware accelerators. The intent of the paper is to provide a thorough survey encompassing various hardware advancements targeting the acceleration of transformer-based architectures commonly utilized in LLMs.

LLMs and their Computational Demands

Transformer networks, introduced in 2017, have catalyzed the development of LLMs owing to their ability to handle long-term dependencies in text through an attention mechanism. The rapid evolution of transformer architectures has scaled up the complexity and capabilities of LLMs. However, this advancement comes at the cost of substantial computational and storage requirements, which has spurred diverse research efforts in hardware acceleration using FPGAs, ASICs, and in-memory computing.

Comprehensive Survey of Hardware Accelerators

The survey is segregated into three facets based on the technological platform: FPGA-based accelerators, CPU and GPU-based accelerators, ASIC-based accelerators, and In-memory hardware accelerators.

FPGA-based Accelerators

Numerous FPGA-based frameworks such as FTRANS, ViA, and STA have shown significant progress in enhancing the computational efficiency of transformers. For instance, FTRANS achieves a performance rate of 170 GOPs and an energy efficiency of 6.8 GOPs/W by employing a Block Circulant Matrix-based weight model, offering 81x acceleration over a GPU (RTX 5000).

STA introduces Sparse Transformer Accelerator models that achieve 392.8 GOPs with STA-4 and 523.8 GOPs with STA-8, exhibiting notable energy efficiency improvements of up to 33.6 GOPs/W and 41.2 GOPs/W respectively, compared to Nvidia's RTX 2080Ti GPUs.

The Swin series, geared towards Vision Transformers, demonstrates significant acceleration across various models, achieving acceleration rates up to 436.4 GOPs, highlighting the versatility of FPGA technology in handling extensive computations inherent in LLMs.

CPU and GPU-based Accelerators

TurboTransformer, for instance, tackles dynamic sequence lengths, providing a 35% increase in response rate by optimizing batching techniques. Another advancement, LightSeq2, integrates fused kernel operators, resulting in 1.4 to 3.5x faster training throughput on GPUs compared to previous systems.

ASIC-based Accelerators

ASIC implementations such as AccelTran (Server) significantly outshine in performance, with an extrapolated performance of 372,000 GOPs, demonstrating the potential of dedicated hardware in achieving substantial computational feats.

Energon, an algorithm-architecture co-design, propounds a mix-precision multi-round filtering algorithm, culminating in a dramatic reduction of computational cost by 4x to 8x with negligible accuracy loss. It achieves up to 8.7x speedup and 1000x energy reduction over an Nvidia V100 GPU.

In-memory Hardware Accelerators

In-memory accelerators like ReTransformer and X-Former leverage non-volatile memory (NVM) technologies to minimize data movement, presenting groundbreaking energy efficiencies such as 467.7 GOPs/W and 13,440 GOPs/W, respectively. These architectures capitalize on integrating computation directly within memory units, thereby curtailing data transfer overheads.

Experimental and Theoretical Extrapolation

The paper undertakes a rigorous quantitative comparison, extrapolating the performance of varied architectures onto a 16nm process technology using both theoretical models and empirical FPGA performance benchmarks. This comparative analysis reveals that ASIC-based designs notably excel in terms of absolute performance, whereas in-memory accelerators distinctly lead in energy efficiency.

Implications and Future Directions

The research posits that the evolution of LLMs will inherently demand more sophisticated hardware solutions. The comparison points towards ASIC and in-memory architectures as prime candidates for future developments, emphasizing the necessity for continued innovation in these domains. The extrapolation to a unified process technology serves as a valuable methodology for fair comparison, which could guide future efforts in optimizing LLM inference and training processes across diverse hardware platforms.

Conclusion

The paper "Hardware Acceleration of LLMs: A Comprehensive Survey and Comparison" provides an exhaustive analysis of the state of hardware accelerations for LLMs, underscoring the critical advancements and identifying key areas for future exploration. The analytical framework set forth paves the way for more uniform evaluations and potentially informs the trajectory for the next generation of efficient, scalable LLM deployments.