MixPE: Quantization and Hardware Co-design for Efficient LLM Inference (2411.16158v1)

Abstract: Transformer-based LLMs have achieved remarkable success as model sizes continue to grow, yet their deployment remains challenging due to significant computational and memory demands. Quantization has emerged as a promising solution, and state-of-the-art quantization algorithms for LLMs introduce the need for mixed-precision matrix multiplication (mpGEMM), where lower-precision weights are multiplied with higher-precision activations. Despite its benefits, current hardware accelerators such as GPUs and TPUs lack native support for efficient mpGEMM, leading to inefficient dequantization operations in the main sequential loop. To address this limitation, we introduce MixPE, a specialized mixed-precision processing element designed for efficient low-bit quantization in LLM inference. MixPE leverages two key innovations to minimize dequantization overhead and unlock the full potential of low-bit quantization. First, recognizing that scale and zero point are shared within each quantization group, we propose performing dequantization after per-group mpGEMM, significantly reducing dequantization overhead. Second, instead of relying on conventional multipliers, MixPE utilizes efficient shift&add operations for multiplication, optimizing both computation and energy efficiency. Our experimental results demonstrate that MixPE surpasses the state-of-the-art quantization accelerators by $2.6\times$ speedup and $1.4\times$ energy reduction.

• The paper demonstrates a novel mixed-precision processing element, MixPE, which achieves a 2.6× speedup and 1.4× energy reduction for LLM inference.
• It introduces an innovative hardware co-design that leverages low-bit computations with efficient shift-add operations to optimize performance and resource usage.
• A design space exploration framework identifies Pareto-optimal configurations, illustrating the integration of quantization techniques with specialized accelerator design.

MixPE: Quantization and Hardware Co-design for Efficient LLM Inference

The paper "MixPE: Quantization and Hardware Co-design for Efficient LLM Inference" addresses the computational and memory challenges associated with deploying LLMs, focusing on improving efficiency in mixed-precision quantization. The authors introduce MixPE, a dedicated mixed-precision processing element specifically designed to enhance low-bit quantization in LLM inference, achieving promising results in terms of speedup and energy reduction compared to current state-of-the-art accelerators.

Overview of the Problem

The rapid growth in LLM sizes has escalated computational and memory requirements, thus hindering deployment in resource-constrained environments. Quantization emerges as an effective strategy to mitigate these demands by representing model weights and activations in lower precision. The state-of-the-art quantization algorithms require mixed-precision matrix multiplication (mpGEMM), where low-precision weights are paired with higher-precision activations. However, mpGEMM is not natively supported by existing hardware such as GPUs and TPUs, leading to performance inefficiencies due to suboptimal dequantization processes.

Key Contributions and Innovations

1. Mixed-Precision Quantization:
   • The authors focus on mixed-precision quantization formats such as W4A8 and W4A16, which offer a balance between memory savings and model accuracy. These formats become particularly appealing due to their ability to preserve model performance while reducing resource consumption.
2. Introduction of MixPE:
   • MixPE is proposed as a specialized processing element that directly performs low-bit quantization computations. It defers dequantization until after per-group mixed-precision GEMM, leveraging shared parameters like scale and zero points within quantization groups to minimize overhead.
   • MixPE uses shift{content}add operations instead of conventional multipliers for efficient multiplication of low-bit weights with high-bit activations. This method enhances both computational speed and energy efficiency.
3. Experimental Evaluation:
   • The authors present a comprehensive evaluation, showcasing a significant $2.6\times$ speedup and $1.4\times$ energy savings with MixPE compared to existing quantization accelerators.
   • A Design Space Exploration (DSE) framework is introduced to evaluate a variety of GEMM accelerators, allowing the identification of Pareto-optimal configurations in terms of numerical accuracy and hardware efficiency.

Practical and Theoretical Implications

• Hardware Efficiency:

The introduction of MixPE represents a substantial progression in hardware design for AI applications, underscoring a path towards more energy-efficient and high-performance inference processes. Its integration into systolic array architectures offers a promising approach to scaling down the energy requirements and area utilization on hardware accelerators.

• Model Deployment:

The techniques advanced in this paper symbolize an enhancement in deploying LLMs across different hardware environments, where limited computational and memory resources typically constrain model performance. This work sets the ground for widespread adoption of LLMs on embedded systems and edge devices.

Future Developments in AI

The concepts elaborated in the paper herald a future where increasingly efficient hardware accelerators can adapt to the demands of dynamic AI workloads. Future work may focus on further optimizing these processes and extending the applicability of such techniques to other model types beyond LLMs. Moreover, as the research community continues to push the boundaries in natural language processing, there exists the potential to integrate similar quantization and hardware innovations into emerging AI frameworks, enhancing both their accessibility and scalability.