Efficient LLM Inference Achieves Speedup with 4-bit Quantization and FPGA Co-Design

The increasing demand for large language models clashes with the limited resources available for their deployment, creating a significant challenge for widespread use. Fen-Yu Hsieh, Yun-Chang Teng, Ding-Yong Hong, and Jan-Jan Wu, all from the Institute of Information Science Academia Sinica, tackle this problem by developing a new automation framework that dramatically reduces the computational and memory demands of these models. Their work introduces a novel hardware-software co-design method, generating specialised accelerators on Field-Programmable Gate Arrays (FPGAs) that combine weight pruning and low-bit quantization techniques. The team demonstrates substantial reductions in model size and significant speedups in processing, achieving lower latency and improved throughput on the widely used LLaMA-7B model, and paving the way for efficient and deployable large language model inference on resource-constrained platforms.

Large language models achieve remarkable performance across a wide range of language processing tasks, but this success demands substantial computational resources and memory, significantly hindering deployment in resource-constrained environments. To overcome this limitation, this work introduces an automation framework that leverages weight pruning and low-bit quantization, alongside a hardware-software co-design method generating accelerators on the Field-Programmable Gate Array (FPGA) platform. Specifically, the team implements a unified pipeline applying N:M structured pruning and 4-bit integer quantization to reduce the memory footprint, followed by optimised dequantization and matrix multiplication to enhance large language model performance. This approach delivers a significant reduction in resource requirements without compromising accuracy, paving the way for wider accessibility and deployment of these powerful models.

Research Method

This research focuses on efficiently deploying large language models (LLMs), addressing the challenges of computational cost and memory footprint. The authors explore a combination of N:M structured sparsity and low-bit quantization as techniques to compress LLMs without significant accuracy loss, also presenting a custom FPGA accelerator designed to take advantage of this sparsity. N:M structured sparsity removes entire rows or columns of weight matrices, allowing for more efficient hardware acceleration by creating regular patterns of zeros and offering more flexibility than fixed sparsity configurations. Low-bit quantization reduces the precision of weights and activations, significantly reducing memory usage and computational requirements.

The team demonstrates that combining N:M sparsity with quantization yields better results than either technique alone. A systolic array-based FPGA accelerator is designed to efficiently perform the matrix multiplications central to LLM inference, specifically handling the N:M sparse matrices created by their compression technique. Scaling analysis using the LLaMA-7B model suggests that the combined sparsity and quantization can improve per-token throughput by up to 1.36x compared to dense execution. The paper focuses on predicted performance gains through analysis and design, with a full empirical evaluation of the FPGA implementation planned as future work.

Pruning and Quantization Accelerate Large Language Models

Scientists have achieved a significant breakthrough in deploying large language models (LLMs) in resource-constrained environments through a novel hardware-software co-design framework. The research team developed an automation pipeline that combines weight pruning and low-bit quantization to dramatically reduce the computational demands of these powerful AI systems. This work focuses on implementing N:M structured pruning alongside 4-bit integer quantization, creating a unified approach applicable across diverse hardware platforms. Experiments demonstrate that utilizing 2:4 sparsity in conjunction with quantization on 4096 × 4096 matrices yields up to a 4× reduction in weight storage requirements.

The team measured a 1.71× speedup in matrix multiplication and a subsequent 1.29× reduction in end-to-end latency when compared to dense GPU baselines. Scaling analysis performed on the LLaMA-7B model revealed that structured sparsity enhances throughput per token by 1.36×, indicating improved processing efficiency as model size increases.

Furthermore, scientists designed and implemented a custom systolic-array-based FPGA accelerator, offering a flexible architectural path to support a wider range of sparsity patterns beyond the limitations of fixed hardware configurations. The FPGA accelerator’s dynamic zero-skipping mechanism and reconfigurable datapath efficiently process generalized N:M sparse and low-bit quantized data, maximizing hardware utilization and throughput while minimizing accuracy degradation. This innovative approach delivers a pathway towards pervasive deep learning, enabling real-time inference and low energy consumption in a variety of applications.

Pruning and Quantization Accelerate Language Models

This work presents a novel framework that successfully integrates weight pruning and low-bit quantization to accelerate large language model inference, addressing a key limitation in deploying these models on devices with limited resources. By combining structured pruning, specifically an N:M approach, with 4-bit integer quantization, the team achieved significant reductions in both memory footprint and computational cost while maintaining a unified compressed representation compatible with various hardware platforms, including CPUs, GPUs, and custom FPGA accelerators. The results demonstrate a substantial decrease in weight storage and a marked speedup in matrix multiplication, ultimately leading to a reduction in end-to-end latency compared to standard GPU processing. Scaling analysis using the LLaMA-7B model further confirms the benefits of this approach, revealing an improvement in per-token throughput, indicating that the advantages of combined pruning and quantization extend to larger, more complex models. Importantly, the developed FPGA accelerator offers a flexible architecture capable of supporting a wider range of sparsity patterns than currently available hardware, paving the way for more adaptable and efficient sparsity-aware computing. Future research will focus on extending the framework to full end-to-end inference and exploring even more advanced sparsity and quantization techniques to further optimize large model deployment.