LlamaF: An Efficient Llama2 Architecture Accelerator on Embedded FPGAs
The paper presents an FPGA-based accelerator for large language models, achieving 14.3-15.8 times speedup and 6.1 times power efficiency, enhancing deployment in resource-constrained environments.
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The paper titled "LlamaF: An Efficient Llama2 Architecture Accelerator on Embedded FPGAs" presents a novel FPGA-based accelerator aimed at enhancing the inference performance of large language models (LLMs) on resource-constrained embedded devices. The authors, Han Xu, Yutong Li, and Shihao Ji, address the challenges posed by the high memory and computational requirements of LLMs. Their approach involves post-training quantization to minimize model size and optimize off-chip memory bandwidth. The design incorporates asynchronous computation and a fully pipelined architecture for matrix-vector multiplication. Experimental results using the TinyLlama 1.1B model on a Xilinx ZCU102 platform demonstrate significant improvements, achieving a speedup of 14.3 to 15.8 times and a power efficiency enhancement of 6.1 times compared to running solely on the ZCU102 processing system. This advancement could facilitate the deployment of LLMs in environments with limited resources, making them more accessible for various applications.
- The paper introduces an FPGA-based accelerator for LLMs on embedded devices.
- It utilizes post-training quantization to reduce model size and improve memory bandwidth.
- The design features asynchronous computation and a fully pipelined architecture.
- Experimental results show a speedup of 14.3-15.8x and a power efficiency improvement of 6.1x.
- The advancements aim to make LLMs more deployable in resource-constrained environments.
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5 commentsThe problem is: seems that their costs is 1x or 2x from what they are charging.
There doesn't seem to be much flux in the low level architectures used for inferencing at this point, so may as well commit to an ASIC, as is already happening with Apple, Qualcomm, etc building NPUs into their SOCs.
Not that I was expecting GPU like efficiency from a fairly small scale FPGA project. Nvidia engineers spent thousands of man-years making sure that stuff works well on GPUs.
but when things plateau off, this, then ASICs, would probably be the most efficient way ahead for "stable" versions of AI models during inference.
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You can easily set up and run large language models (LLMs) on your PC using tools like Ollama, LM Suite, and Llama.cpp. Ollama supports AMD GPUs and AVX2-compatible CPUs, with straightforward installation across different systems. It offers commands for managing models and now supports select AMD Radeon cards.
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The article explains how to run the Llama 3.1 405B model on home devices using the Distributed Llama project, detailing setup, resource requirements, and methods for efficient execution across multiple devices.
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The paper reviews hardware acceleration techniques for Large Language Models, comparing frameworks across platforms like FPGA and GPU, addressing evaluation challenges, and contributing to advancements in natural language processing.
How to evaluate performance of LLM inference frameworks
LLM inference frameworks face a "memory wall" limiting performance. Developers should choose frameworks wisely, apply optimizations cautiously, and structure applications for server or offline scenarios to enhance efficiency.
Fine-Tuning LLMs to 1.58bit
BitNet introduces extreme quantization for large language models, achieving 1.58 bits per parameter, enhancing efficiency and performance, particularly in fine-tuning Llama3 8B models while integrating into existing frameworks.