NeuPIMs: Heterogeneous Acceleration for Large Language Models

Core Concepts

NeuPIMs proposes a novel heterogeneous accelerator system that combines NPU and PIM devices to enhance the efficiency of large language model inference.

Abstract

NeuPIMs introduces a system that optimizes GEMM and GEMV computations in Large Language Models (LLMs). By combining NPUs and PIM technology, NeuPIMs achieves significant throughput improvements compared to existing systems. The proposed hardware-algorithm co-design approach addresses microarchitectural and algorithmic challenges, resulting in enhanced resource utilization and overall efficiency.

Stats

NeuPIMs achieves 2.3× throughput improvement over an NPU-only approach. Compared to a naïve NPU-PIM integrated system, NeuPIMs achieves a 1.6× throughput improvement.

Quotes

Key Insights Distilled From

NeuPIMs

by Guseul Heo,S... at arxiv.org 03-04-2024

https://arxiv.org/pdf/2403.00579.pdf

Deeper Inquiries

How can NeuPIMs be adapted for other types of machine learning models?

NeuPIMs can be adapted for other types of machine learning models by leveraging its heterogeneous accelerator-based system architecture that combines a conventional NPU focused on GEMM computations with PIM devices optimized for GEMV operations. This approach allows for efficient utilization of memory bandwidth, computational resources, and memory capacity in the system to improve overall inference throughput. To adapt NeuPIMs to different machine learning models, one would need to analyze the specific computational characteristics of the model and identify the key components that require compute-intensive matrix-matrix multiplications (GEMM) and bandwidth-heavy matrix-vector multiplications (GEMV). By understanding these requirements, one can design a tailored hardware-algorithm co-design approach similar to NeuPIMs but customized to suit the specific needs of the new machine learning model.

What are the potential drawbacks or limitations of integrating NPUs and PIM technology?

While integrating NPUs and PIM technology offers significant advantages in terms of improving inference throughput for large language models like LLMs, there are also potential drawbacks and limitations to consider: Complexity: Integrating two different technologies like NPUs and PIM devices requires sophisticated hardware design and software optimization efforts. Synchronization Challenges: Coordinating concurrent operations between NPUs and PIM devices while addressing specific kernel types can introduce synchronization challenges that may impact overall efficiency. Algorithmic Dependencies: The inherent dependencies between GEMM and GEMV operations in certain machine learning models could limit parallel processing capabilities when using both NPUs and PIM technology simultaneously. Resource Utilization: Ensuring optimal resource utilization on both platforms without underutilizing either NPU or PIM resources is crucial but challenging.

How might NeuPIMS impact the future development of large language models?

NeuPIMS has the potential to significantly impact future developments in large language models by offering a novel solution that addresses key challenges related to inference efficiency. Some ways in which NeuPIMS might influence future LLM development include: Improved Throughput: By achieving 2.3× throughput improvement compared to an NPU-only approach, NeuPIMS demonstrates how combining NPU-centric compute power with PIM-optimized bandwidth handling can enhance overall performance. Efficient Resource Utilization: With increased resource utilization on both NPU (57%) and PIP (22%), NeuPIMS sets a precedent for balancing workload distribution effectively across multiple accelerators. Hardware-Algorithm Co-design Approach: The microarchitectural innovations introduced by NeuPIMS pave the way for more efficient integration strategies between different acceleration technologies, inspiring further research into optimizing hardware-software interactions. Overall, NeuPIMS serves as a promising example of how heterogeneous acceleration systems can push boundaries in enhancing inference efficiency for complex machine learning tasks like those encountered in large language model applications such as GPT4 or LLaMA.