Designing Efficient LLM Accelerators for Edge Devices
(2408.00462)Abstract
The increase in open-source availability of LLMs has enabled users to deploy them on more and more resource-constrained edge devices to reduce reliance on network connections and provide more privacy. However, the high computation and memory demands of LLMs make their execution on resource-constrained edge devices challenging and inefficient. To address this issue, designing new and efficient edge accelerators for LLM inference is crucial. FPGA-based accelerators are ideal for LLM acceleration due to their reconfigurability, as they enable model-specific optimizations and higher performance per watt. However, creating and integrating FPGA-based accelerators for LLMs (particularly on edge devices) has proven challenging, mainly due to the limited hardware design flows for LLMs in existing FPGA platforms. To tackle this issue, in this paper we first propose a new design platform, named SECDA-LLM, that utilizes the SECDA methodology to streamline the process of designing, integrating, and deploying efficient FPGA-based LLM accelerators for the llama.cpp inference framework. We then demonstrate, through a case study, the potential benefits of SECDA-LLM by creating a new MatMul accelerator that supports block floating point quantized operations for LLMs. Our initial accelerator design, deployed on the PYNQ-Z1 board, reduces latency 1.7 seconds per token or ~2 seconds per word) by 11x over the dual-core Arm NEON-based CPU execution for the TinyLlama model.
Overview
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The paper presents SECDA-LLM, a framework employing FPGAs to enhance the efficiency of LLM inference on resource-constrained edge devices.
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A case study with the TinyLlama model demonstrates a significant performance improvement, achieving an 11x speedup over traditional CPU methods through the use of a specialized MatMul accelerator.
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The research highlights future potential in expanding SECDA-LLM as an open-source platform for collaborative development and explores further architectural optimizations.
Efficient FPGA-based Accelerators for LLM Inference on Edge Devices
The paper "Designing Efficient LLM Accelerators for Edge Devices" addresses the significant challenges associated with deploying computationally intensive LLMs on resource-constrained edge devices. The primary focus is on designing FPGA-based accelerators to improve the efficiency of LLM inference. This essay provides a detailed summary of the paper, presenting its core contributions, methodological approaches, and implications for future research.
Introduction
The rapid growth and open-source availability of LLMs, such as GPT-3, have positioned them at the forefront of advancements in NLP. However, the computational and memory demands of these models pose substantial challenges when executing them on edge devices with limited resources. Traditional CPU- or GPU-based methods for LLM inference are often infeasible on edge devices due to these constraints. The paper proposes utilizing FPGAs for LLM acceleration, leveraging their reconfigurability to achieve model-specific optimizations and enhanced performance per watt.
Proposed Framework: SECDA-LLM
To tackle the integration hurdles of FPGA-based LLM accelerators, the paper introduces SECDA-LLM, a design platform guided by the SECDA (SystemC Enabled Co-design of DNN Accelerators) methodology. SECDA-LLM streamlines the design, integration, and deployment process of efficient FPGA-based accelerators within the llama.cpp inference framework.
Design Methodology
SECDA-LLM builds upon the core llama.cpp project to facilitate seamless integration between FPGA accelerators and the inference framework. The platform supports rapid prototyping using SystemC, with the following key features:
- Integration with llama.cpp: SECDA-LLM connects the llama.cpp GGML library to the FPGA accelerator through a context handler that facilitates data and parameter exchange.
- SystemC Simulation: End-to-end simulation is utilized for prototyping, leveraging SystemC for efficient design iteration and performance profiling.
- Hardware Evaluation: The platform enables hardware synthesis after SystemC simulation, allowing for real hardware execution without the need to modify driver code significantly.
- Profiling Tools: Comprehensive profiling capabilities are provided for both simulation and actual hardware execution, aiding in performance analysis and bottleneck identification.
Case Study: MatMul Accelerator for TinyLlama
The effectiveness of SECDA-LLM is demonstrated through a case study involving the development of a MatMul accelerator designed to support block floating point (BFP) quantized operations. Targeting the TinyLlama model, the accelerator was implemented and evaluated on a PYNQ-Z1 board, achieving a notable 11x speedup over dual-core ARM NEON-based CPU execution.
Design Details
The accelerator features several key components:
- Instruction Decoder: Loads and decodes instructions from the AXI-Stream.
- Data Mapper: Efficiently parses and maps data into weight and input buffers.
- Super-Block Vector Processor (SBVP): Computes the dot product of quantized weights and inputs.
- Scheduler: Manages MatMul operation tiling and synchronizes data transfers.
The TinyLlama model, which was quantized to the Q3 format with block floating point quantization, demonstrated a significant reduction in inference latency, thus improving the feasibility of running LLMs on edge devices.
Implications and Future Directions
The SECDA-LLM platform represents a meaningful step towards enabling efficient LLM inference on edge devices. By combining the reconfigurability of FPGAs with the methodological rigor of SECDA, the framework offers a robust solution for developing specialized hardware accelerators. The quantitative results underscore the potential of FPGA-based accelerators to meet the computational demands of modern LLMs while operating within the constraints of edge devices.
Future work could expand the scope of SECDA-LLM into an open-source platform to foster collaborative development and continuous enhancement of LLM performance on resource-constrained devices. Additionally, further research could explore architectural optimizations and broader applications within the diverse ecosystem of edge computing.
Conclusion
The paper successfully presents SECDA-LLM as an efficient and practical framework for designing FPGA-based accelerators tailored for LLMs on edge devices. The case study highlights substantial performance improvements, underscoring the framework's potential to address the computational challenges of LLM inference. SECDA-LLM sets the stage for future advancements in deploying powerful AI models in real-world, resource-constrained environments, marking an important contribution to the field of edge computing.
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